text:
This is a press release submitted to the Las Vegas Sun. It has not been verified or edited by the Sun.
Press Release
Mayor of Las Vegas, Carolyn Goodman, honors officers of the Las Vegas Metropolitan Police Department
Published on Tue, Jun 24, 2014 (11:27 p.m.)On July 12, 2014 Mayor Carolyn Goodman will honor several Police Officers from the Las Vegas Metropolitan Police Department and recognize a school counselor for their efforts in stopping youth from dropping out of school and their dedication to the, "Drop out Recovery Program". This is an unprecedented event that will be taking place! The efforts of the faith community and Faith Base has also help curb the violence in the community. Local churches from around the valley are now uniting with local Metro Area Command Centers and Mayor's Faith Initiative by signing up to assist in changing our City. Never before has so many Religious Based organizations partnered together; to reach out to our community in times of distress to address issues of Violence, Addiction, Strengthen families, Jobs, Education, Homelessness and Human Trafficking. The Mayor's Faith Initiative has made it all possible, with the assistance from Local Church Leaders. Please join us.....
Date: July 12, 2014
Time: 10am-12noon
Where: East Vegas Community Center, 2500 North Eastern Ave, Las Vegas, Nevada
Most Popular
- Viewed
- Discussed
- E-mailed
- Golden Knights hire new chief marketing officer
- Man stabs son, 4, to death in murder-suicide in southwest valley
- Police: One-armed, machete-wielding clown arrested
- Heller votes against Obamacare repeal, delay
- Nevada lawmakers denounce Trump ban on transgender troops


text:
Butterick 1420; ca. 1941; Junior and Girls' Two-Piece Frock. Blouse in two lengths. Two-piece flared or pleated skirt. Transfer, Instructions for Embroidery and Color Combinations.
Links to reviews/blog postsEdit
- New Vintage Studio sz 12
Add a photo to the gallery by clicking the "modify" button below.
[insert your username, and make sure your preferences allow for people to contact you via e-mail. Do not list your email address here!]

subset: commoncrawl


text:
F β Support Vector Machines
- Beatrice Watts
- 2 years ago
- Views:
1 Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 - August 4, 2005 F β Support Vector Machines Jérôme Callut and Pierre Dupont Department of Computing Science and Engineering, INGI Université catholique de Louvain, Place Sainte-Barbe 2 B-1348 Louvain-la-Neuve, Belgium Abstract We introduce in this paper F β SVMs, a new parametrization of support vector machines. It allows to optimize a SVM in terms of F β, a classical information retrieval criterion, instead of the usual classification rate. Experiments illustrate the advantages of this approach with respect to the traditionnal 2- norm soft-margin SVM when precision and recall are of unequal importance. An automatic model selection procedure based on the generalization F β score is introduced. It relies on the results of Chapelle, Vapnik et al. [4] about the use of gradient-based techniques in SVM model selection. The derivatives of a F β loss function with respect to the hyperparameters C and the width σ of a gaussian kernel are formally defined. The model is then selected by performing a gradient descent of the F β loss function over the set of hyperparameters. Experiments on artificial and real-life data show the benefits of this method when the F β score is considered. I. INTRODUCTION Support Vector Machines (SVM) introduced by Vapnik [18] have been widely used in the field of pattern recognition for the last decade. The popularity of the method relies on its strong theoretical foundations as well as on its practical results. Performance of classifiers is usually assessed by means of classification error rate or by Information Retrieval (IR) measures such as precision, recall, F β, breakeven-point and ROC curves. Unfortunately, there is no direct connection between these IR criteria and the SVM hyperparameters: the regularization constant C and the kernel parameters. In this paper, we propose a novel method allowing the user to specify his requirement in terms of the F β criterion. First of all, the F β measure is reviewed as a user specification criterion in section II. A new SVM parametrization dealing with the β parameter is introduced in section III. Afterwards, a procedure for automatic model selection according to F β is proposed in section IV. This procedure is a gradient-based technique derived from the results of Chapelle, Vapnik et al. [4]. Finally, experiments with artifical and real-life data are presented in section V. II. USER SPECIFICATIONS WITH THE F β CRITERION Precision and recall are popular measures to assess classifiers performance in an information retrieval context [16]. Therefore, it would be convenient to use these evaluation criteria when formulating the user specifications. For instance, let us consider the design of a classifier used to retrieve documents according to topic. Some users prefer to receive a limited list of relevant documents even if this means losing some interesting ones. Others would not want to miss any relevant document at the cost of also receiving non-relevant ones. Those specifications correspond respectively to a high precision and a high recall. The two previous measures can be combined in a unique F β measure in which the paramater β specifies the relative importance of recall with respect to precision. Setting β equals to 0 would only consider precision whereas taking β = would only take recall into account. Moreover, precision and recall are of equal importance when using the F 1 measure. The contingency matrix and estimations of precision, recall and F β are given hereafter. Target: +1 Target: True Pos. (#TP) False Pos. (#FP) -1 False Neg. (#FN) True Neg. (#TN) Precision π Recall ρ F β #TP #TP+#FP #TP #TP+#FN (β 2 +1)πρ β 2 π+ρ III. F β SUPPORT VECTOR MACHINES In this section, we introduce a new parametrization of SVM allowing to formulate user specifications in terms of the F β criterion. To do so, we establish a relation between the contingency matrix and the slack variables used in the soft-margin SVM setting. Based on this link, we devise a new optimization problem which maximizes an approximation of the F β criterion regularized by the size of the margin. A. Link between the contingency matrix and the slacks Let us consider a binary classification task with a training set Tr = {(x 1,y 1 ),..., (x n,y n )} where x i is an instance in some input space X and y i { 1, +1} represents its category. Let n + and n denote respectively the number of positive and negative examples. The soft-margin formulation of SVM allows examples to be missclassified or to lie inside the margin by the introduction of slack variables ξ in the problem constraints: OP1 Minimize W (w,b,ξ) = 1 2 w 2 + C.Φ(ξ) /05/$ IEEE 1443
2 s.t. { yi ( w, x i + b) 1 ξ i i =1..n ξ i 0 i =1..n where w and b are the parameters of the hyperplane. The Φ(.) term introduced in the objective function is used to penalize solutions presenting many training errors. For any feasible solution (w,b,ξ), missclassified training examples have an associated slack value of at least 1. The situation is illustrated in figure 1. Hence, it seems natural to chose a function counting the number of slacks greater or equal to 1 as penalization function Φ(.). Unfortunately, the optimization of such a function combined with the margin criterion turns out to be a mixed-integer problem known to be NP-hard [15]. In fact, two approximations of the counting function are commonly used: Φ(ξ) = n i=1 ξ i (1-norm) and Φ(ξ) = n i=1 ξ2 i (2- norm). These approximations present two peculiarities: 1) The sum of slacks related to examples inside the margin might be considered as errors. 2) Examples with a slack value greater than 1 might contribute as more than one error. However, the use of these approximations is computationally attractive as the problem remains convex, quadratic and consequently solvable in polynomial time. In the sequel, we will focus on the 2-norm alternative. Fig. 1. Soft-margin SVM and associated slacks The computation of the preceding approximations separately for different class labels allows to bound the elements of the contingency matrix. Proposition 1: Let (w,b,ξ) be a solution satisfying the constraints of OP1. The following bounds holds for the elements of the contingency matrix computed on the training set: #TP n + ξi 2 #FP ξi 2 #FN ξi 2 #TN n ξi 2 These bounds will be called the slack estimates of the contingency matrix. It should be noted that they also could have been formulated using the 1-norm approximation. B. The F β parametrization Let us introduce a parametrization of SVM in which a regularized F β criterion is optimized. The F β function can be expanded using the definition of precision and recall as: F β = (β2 +1)πρ β 2 π + ρ = (β 2 +1)#TP (β 2 +1)#TP + β 2 #FN +#FP The optimal value for F β ( 1) is obtained by minimizing β 2 #FN +#FP. Replacing #FN and #FP by their slack estimates and integrating this into the objective function leads to the following optimization problem: OP2 Minimize W (w,b,ξ) = 1 2 w 2 + C.[β 2. ξ 2 i + { yi ( w, x s.t. i + b) 1 ξ i i =1..n ξ i 0 i =1..n The relative importance of the F β criterion with respect to the margin can be tuned using the regularization constant C. Since the slack estimates for #FP and #FN are upper bounds, OP2 is based on a pessimistic estimation of the F β. OP2 can be seen as an instance of the SVM parametrization considering two kinds of slacks with the associated regularization constants C + and C [21], [13]. In our case, the regularization constants derive from the β value, i.e. C + = Cβ 2 and C = C. It should be pointed out that when β =1, OP2 is equivalent to the traditional 2-norm soft-margin SVM problem. The optimization of the F β criterion is closely related to the problem of training a SVM with an imbalanced dataset. When the prior of a class is by far larger than the prior of the other class, the classifier obtained by a standard SVM training is likely to act as the trivial acceptor/rejector (i.e. a classifier always predicting +1, respectively 1). To avoid this inconvenience, some authors [21] have introduced different penalities for the different classes using C + and C. This method has been applied in order to control the sensitivity 1 of the model. However, no automatic procedure has been proposed to choose the regularization constants with respect to the user specifications. Recently, this technique has been improved by artificially oversampling the minority class [1]. Other authors [2] have proposed to select a unique regularization constant C through a bootstrap procedure. This constant is then used as a starting point for tuning C + and C on a validation set. IV. MODEL SELECTION ACCORDING TO F β In the preceding section, we proposed a parametrization of SVM enabling the user to formulate his specifications with the β parameter. In addition, the remaining hyperparameters, i.e. the regularization constant and the kernels parameters, must be selected. In the case of SVM, model selection can be made using the statistical properties of the optimal hyperplane, thus avoiding the need of performing cross-validation. Indeed, 1 The sensitivity is the rate of true positive examples and is equivalent to recall. ξ 2 i ] 1444
3 several bounds of the leave-one-out (loo) error rate can be directly derived from the parameters of the optimal hyperplane expressed in dual form [20], [14], [10]. A practical evaluation of several of these bounds has been recently proposed in [7]. Moreover, Chapelle, Vapnik et al. [4] have shown that the hyperplane dual parameters are differentiable with respect to the hyperparameters. This allows the use of gradient-based techniques for model selection [4], [5]. In this section, we propose a gradient-based algorithm selecting automatically C and the width σ of a gaussian kernel 2 according to the generalization F β score. A. The generalization F β loss function It has been proved by Vapnik [19] that for an example (x i,y i ) producing a loo error, 4α i R 2 1 holds, where R is the radius of the smallest sphere enclosing all the training examples and α i is the i-th dual parameter of the optimal hyperplane. This inequality was originally formulated for the hard-margin case. However, it can be applied to the 2-norm soft-margin SVM as the latter can be seen as a hard margin problem with a transformed kernel [6], [13]. Using the preceding inequality, one can build an estimator of the generalization F β score of a given model. Alternately, it is possible to formulate a loss function following the reasoning developed in section III-B: L Fβ (α,r) 4R 2 β 2 α i + α i In the algorithm proposed in section IV-B, the model parameters are selected by minimizing the L Fβ (.,.) loss function. B. The model selection algorithm We introduce here an algorithm performing automatic model selection according to the F β criterion. It selects the model by performing a gradient descent of the F β loss function over the set of hyperparameters. For the sake of clarity, C and σ, are gathered in a single vector θ. The model selection algorithm is sketched hereafter. 2 k(x i, x j )=exp( x i x j 2 /2σ 2 ) Algorithm F β MODELSELECTION Input: Training set Tr =(x 1,y 1 ),...,(x n,y n ) Initial values for the hyperparameters θ 0 Precision parameter ɛ Output: Optimal hyperparameters θ SVM optimal solution α using θ α 0 trainf β SVM(Tr,θ 0 ); (R, λ) 0 smallestsphereradius(tr,θ 0 ); repeat θ t+1 updatehyperparameters(θ t, α t,r t, λ t ); α t+1 trainf β SVM(Tr,θ t+1 ); (R, λ) t+1 smallestsphereradius(tr,θ t+1 ); t t +1; until L Fβ (α t,r t ) L Fβ (α t 1,R t 1 ) <ɛ; return {θ t, α t } The trainf β SVM function solves OP3, the dual problem of OP2, which has the same form as the dual hard-margin problem [15]: OP3 Maximize W (α) = 1 n n α i α j y i y j k (x i, x j )+ α i 2 i,j=1 i=1 { n s.t. i=1 α iy i =0 α i 0 i =1..n with a transformed kernel: { k 1 k(xi, x j )+δ ij. (x i, x j )= Cβ if y 2 i =+1 k(x i, x j )+δ ij. 1 C if y i = 1 where δ ij is the Kronecker delta and k(.,.) is the original kernel function. The radius of the smallest sphere enclosing all the examples computed by the smallestsphereradius function is obtained by taking the square root of the objective function optimal value in the following optimization problem [15]: OP4 Maximize n W (λ) = λ i k (x i, x i ) i=1 n λ i λ j k (x i, x j ) i,j=1 { n s.t. i=1 λ i =1 λ i 0 i =1..n The optimization problems OP3 and OP4 can be solved in polynomial time in n, e.g. using an interior point method [17]. Furthermore, the solution to OP3, respectively OP4, at a given iteration can be used as a good starting point for the next iteration. 1445
4 At each iteration, the hyperparameters can be updated by means of a gradient step : θ t+1 = θ t η. L Fβ / where η>0 is the updating rate. However, second order methods often provide a faster convergence, which is valuable since two optimization problems have to be solved at each iteration. For this reason, the updatehyperparameters function relies on the BFGS algorithm [8], a quasi-newton optimization technique. The time complexity of the updatehyperparameters function is O(n 3 ) since it is dominated by the inversion of a possibly n n matrix (see section IV-C). The derivatives of the F β loss function with respect to the hyperparameters are detailed in the next section. The algorithm is iterated until the F β loss function no longer changes by more than ɛ. negative classes were respectively 0.3 and 0.7. It is usually more difficult to obtain a good recall when data are unbalanced in this way. Experiments were carried out using training sets of 600 examples, a fixed test set of 1,000 examples and a linear kernel. A comparison between the F β parametrization and the 2-norm soft-margin SVM with C =1is displayed in figure 2. For each β considered, the training data were resampled 10 times in order to produce averaged results. In this setting, our parametrization obtained better F β scores than the standard soft-margin SVM, especially when a high recall was requested. The second part of the figure 2 presents the evolution of precision, recall and the F β score for different β values. C. Derivatives of the F β loss function The derivatives of the transformed kernel function with respect to the hyperparameters are given by: k (x i, x j ) 1/(C 2 β 2 ) if i = j and y i =+1 = 1/C 2 if i = j and y i = 1 C 0 otherwise k (x i, x j ) σ 2 = k(x i, x j ) x i x j 2 2σ 4 The derivatives of the squared radius can then be obtained applying the lemma 2 of Chapelle, Vapnik et al. [4]: R 2 n = k (x i, x i ) n k (x i, x j ) λ i λ i λ j i=1 i,j=1 where θ {C, σ 2 }. The derivation of the hyperplane dual parameters proposed in [4] follows: ( ) (α,b) 1 H y = H (α,b)t, H = T Ky y y T 0 where K is the kernel matrix and y is the vector of examples labels. The H matrix is derived by using the preceding kernel function derivatives. It should be stressed that only examples corresponding to support vectors have to be considered in the above formula. Finally, the derivative of L Fβ (.,.) with respect to a hyperparameter θ is given by: L Fβ (α,r) = 4 R2 β 2 + 4R 2 β 2 α i + α i + α i α i V. EXPERIMENTS We performed several experiments to assess the performance of the F β parametrization and the model selection algorithm. First, the F β parametrization was tested with positive and negative data in R 10 drawn from two largely overlapping normal distributions. The priors for positive and Fig. 2. The F β parametrization tested with artificially generated data. Top: comparison between the standard 2-norm soft-margin SVM and the F β parametrization. Bottom: Evolution of precision, recall and of the F β score accoring to different β values. Afterwards, our parametrization was tested using several class priors. The experimental setup was unchanged except for the class priors while generating the training and test data. Figure 3 shows the evolution of the F β score obtained by our parametrization and by the 2-norm soft-margin SVM using several class priors. For the standard 2-norm soft-margin SVM, one notes that the effect of the priors is particularly important when positive examples are few in numbers and that a high recall is requested. In this setting, our parametrization outperformed the standard 2-norm soft-margin SVM by more than 0.1. The model selection algorithm was first tested with data 1446
5 Fig. 3. The F β parametrization tested using artificially generated data with several class priors. Top: F β scores obtained on the test set using the F β parametrization. Bottom: F β scores obtained on the test set using the standard 2-norm soft-margin SVM. Fig. 4. The F β model selection algorithm tested with artificially generated data and with β =2. Top: the evolution of the F β loss function during the gradient descent. Bottom: the related values of precision, recall and F β score on independent test data. generated as in the previous paragraph. The hyperparameters C and σ were initialized to 1 and the precision parameter ɛ was set to Our objective was to investigate the relation between the minimization of the F β loss function and the F β score obtained on unknown test data. The figure 4 shows the evolution of the F β loss function during the gradient descent, using β =2. The associated precision, recall and F β scores on test data are displayed in the bottom of the figure 4. Even if the optima of the F β loss function and the F β score do not match exactly, one can observe that good F β scores were obtained when the F β loss function is low. After 35 iterations, the classifier obtained a F β score close to 0.9 with the hyperparameters C = 4.33 and σ = The model selection algorithm was then compared to the Radius-Margin (RM) based algorithm [4] using the Diabetes dataset [3]. This dataset contains 500 positive examples and 268 negative examples. It was randomly split into a training and a test set, each one containing 384 examples. In this setting, it is usually more difficult to obtain a classifier with a high precision. The same initial conditions as before were used. The RM based algorithm select the model parameters of the 2-norm soft-margin SVM according to the RM estimator of the generalization error rate. It should be pointed out that when β =1, both methods are equivalent since the same function is optimized. The comparison is illustrated in the first part of the figure 5. As expected, our method provided better results when β moves far away from value 1. The influence of the β parameter on precision, recall and the F β score can be observed in the second part of the figure 5. VI. CONCLUSION We introduced in this paper F β SVMs, a new parametrization of support vector machines. It allows to formulate user specifications in terms of F β, a classical IR measure. Experiments illustrates the benefits of this approach over a standard SVM when precision and recall are of unequal importance. Besides, we extended the results of Chapelle, Vapnik et al. [4] based on the Radius-Margin (RM) bound in order to automatically select the model hyperparameters according to the generalization F β score. We proposed an algorithm which performs a gradient descent of the F β loss function over the set of hyperparameters. To do so, the partial derivatives of the F β loss function with respect to these hyperparameters have been formally defined. Our experiments on real-life data show the advantages of this method compared to the RM based algorithm when the F β evaluation criterion is considered. Our future work includes improvements to the model selection algorithm in order to deal with larger training sets. Indeed, it is possible to use a sequential optimization method [11] in the smallestsphereradius function and chunking 1447
6 Fig. 5. The F β model selection algorithm tested with the Diabetes dataset. Top: Comparison between the F β model selection algorithm and the radiusmargin based method. Bottom: Evolution of precision, recall and of the F β score accoring to different β values. techniques [9], [15] in the trainf β SVM function. This typically allows to solve problems with more than 10 4 variables. Moreover, we believe that the inverse matrix H 1 can be computed incrementally during the chuncking iterations, using the Schur inversion formula for block matrices [12]. [7] Kaibo Duan, S. Sathiya Keerthi, and Aun Neow Poo. Evaluation of simple performance measures for tuning svm hyperparameters. Neurocomputing, 51:41 59, [8] R. Fletcher and M. J. D. Powell. A rapidly convergent descent method for minimization. Computer Journal, 6: , [9] T. Joachims. Making large-scale support vector machine learning practical. In A. Smola B. Scholkopf, C. Burges, editor, Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge, MA, [10] Thorsten Joachims. Estimating the generalization performance of a SVM efficiently. In Pat Langley, editor, Proceedings of ICML-00, 17th International Conference on Machine Learning, pages , Stanford, US, Morgan Kaufmann Publishers, San Francisco, US. [11] S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE-NN, 11(1):124, January [12] Carl D. Meyer. Matrix analysis and applied linear algebra. Society for Industrial and Applied Mathematics, [13] John Shawe-Taylor Nello Critianini. An Introduction to Support Vector Machines. The Press Syndicate of the University of Cambridge, [14] B. Schölkopf, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Generalization bounds via eigenvalues of the Gram matrix. Technical report, Australian National University, February Submitted to COLT99. [15] B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, Cambridge, [16] Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1 47, [17] R. Vanderbei. LOQO: An interior point code for quadratic programming. Technical Report SOR 94-15, Princeton University, [18] Vladimir Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, [19] Vladimir Vapnik. Statistical Learning Theory. Wiley-Interscience, New York, [20] Vladimir Vapnik and Olivier Chapelle. Bounds on error expectation for support vector machines. Neural Computation, 12(9), [21] K. Veropoulos, N. Cristianini, and C. Campbell. Controlling the sensitivity of support vector machines. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI99), Stockholm, Sweden, ACKNOWLEDGMENT This work is partially supported by the Fonds pour la formation à la Recherchedans l Industrieet dans l Agriculture (F.R.I.A.) under grant reference F3/5/5-MCF/FC REFERENCES [1] Rehan Akbani, Stephen Kwek, and Nathalie Japkowicz. Applying support vector machines to imbalanced datasets. In Proceedings of the 15th European Conference on Machine Learning (ECML), pages 39 50, Pisa, Italy, [2] S. Amerio, D. Anguita, I. Lazzizzera, S. Ridella, F. Rivieccio, and R. Zunino. Model selection in top quark tagging with a support vector classifier. In Proceedings of International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, July [3] C.L. Blake and C.J. Merz. UCI repository of machine learning databases, [4] Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, and Sayan Mukherjee. Choosing multiple parameters for support vector machines. Machine Learning, 46(1-3): , [5] Kai-Min Chung, Wei-Chun Kao, Chia-Liang Sun, Li-Lun Wang, and Chih-Jen Lin. Radius margin bounds for support vector machines with the rbf kernel. Neural Comput., 15(11): , [6] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine Learning, 20(3): ,
Support Vector Machines with Clustering for Training with Very Large Datasets
Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France firstname.lastname@example.com MassimilianoMore information
Knowledge Discovery from patents using KMX Text Analytics
Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs firstname.lastname@example.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchersMore information
Regression Using Support Vector Machines: Basic Foundations
Regression Using Support Vector Machines: Basic Foundations Technical Report December 2004 Aly Farag and Refaat M Mohamed Computer Vision and Image Processing Laboratory Electrical and Computer EngineeringMore information
Choosing Multiple Parameters for Support Vector Machines
Machine Learning, 46, 131 159, 2002 c 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Choosing Multiple Parameters for Support Vector Machines OLIVIER CHAPELLE LIP6, Paris, France email@example.com information
Introduction to Support Vector Machines. Colin Campbell, Bristol University
Introduction to Support Vector Machines Colin Campbell, Bristol University 1 Outline of talk. Part 1. An Introduction to SVMs 1.1. SVMs for binary classification. 1.2. Soft margins and multi-class classification.More information
Controlling the Sensitivity of Support Vector Machines. K. Veropoulos, C. Campbell, N. Cristianini. United Kingdom. maximising the Lagrangian:
Controlling the Sensitivity of Support Vector Machines K. Veropoulos, C. Campbell, N. Cristianini Department of Engineering Mathematics, Bristol University, Bristol BS8 1TR, United Kingdom Abstract ForMore information
Support Vector Machines Explained
March 1, 2009 Support Vector Machines Explained Tristan Fletcher www.cs.ucl.ac.uk/staff/t.fletcher/ Introduction This document has been written in an attempt to make the Support Vector Machines (SVM),More information
Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms
Comparing the Results of Support Vector Machines with Traditional Data Mining Algorithms Scott Pion and Lutz Hamel Abstract This paper presents the results of a series of analyses performed on direct mailMore information
E-commerce Transaction Anomaly Classification
E-commerce Transaction Anomaly Classification Minyong Lee firstname.lastname@example.com Seunghee Ham firstname.lastname@example.com Qiyi Jiang firstname.lastname@example.com I. INTRODUCTION Due to the increasing popularity of e-commerceMore information
Which Is the Best Multiclass SVM Method? An Empirical Study
Which Is the Best Multiclass SVM Method? An Empirical Study Kai-Bo Duan 1 and S. Sathiya Keerthi 2 1 BioInformatics Research Centre, Nanyang Technological University, Nanyang Avenue, Singapore 639798 firstname.lastname@example.com information
Support Vector Machine (SVM)
Support Vector Machine (SVM) CE-725: Statistical Pattern Recognition Sharif University of Technology Spring 2013 Soleymani Outline Margin concept Hard-Margin SVM Soft-Margin SVM Dual Problems of Hard-MarginMore information
Support Vector Machine. Tutorial. (and Statistical Learning Theory)
Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America 4 Independence Way, Princeton, USA. email@example.com 1 Support Vector Machines: history SVMs introducedMore information
Large Margin DAGs for Multiclass Classification
S.A. Solla, T.K. Leen and K.-R. Müller (eds.), 57 55, MIT Press (000) Large Margin DAGs for Multiclass Classification John C. Platt Microsoft Research Microsoft Way Redmond, WA 9805 firstname.lastname@example.com information
Using Random Forest to Learn Imbalanced Data
Using Random Forest to Learn Imbalanced Data Chao Chen, email@example.com Department of Statistics,UC Berkeley Andy Liaw, andy email@example.com Biometrics Research,Merck Research Labs Leo Breiman,More information
Adaptive Online Gradient Descent
Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650More information
Active Learning in the Drug Discovery Process
Active Learning in the Drug Discovery Process Manfred K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen Computer Science Dep., Univ. of Calif. at Santa Cruz FHG FIRST, Kekuléstr.More information
Predict Influencers in the Social Network
Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, email@example.com Department of Electrical Engineering, Stanford University Abstract Given two personsMore information
SVM Ensemble Model for Investment Prediction
19 SVM Ensemble Model for Investment Prediction Chandra J, Assistant Professor, Department of Computer Science, Christ University, Bangalore Siji T. Mathew, Research Scholar, Christ University, Dept ofMore information
Towards better accuracy for Spam predictions
Towards better accuracy for Spam predictions Chengyan Zhao Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S 2E4 email@example.com Abstract Spam identification is crucialMore information
A fast multi-class SVM learning method for huge databases
www.ijcsi.org 544 A fast multi-class SVM learning method for huge databases Djeffal Abdelhamid 1, Babahenini Mohamed Chaouki 2 and Taleb-Ahmed Abdelmalik 3 1,2 Computer science department, LESIA Laboratory,More information
Comparing Support Vector Machines, Recurrent Networks and Finite State Transducers for Classifying Spoken Utterances
Comparing Support Vector Machines, Recurrent Networks and Finite State Transducers for Classifying Spoken Utterances Sheila Garfield and Stefan Wermter University of Sunderland, School of Computing andMore information
An Introduction to Machine Learning
An Introduction to Machine Learning L5: Novelty Detection and Regression Alexander J. Smola Statistical Machine Learning Program Canberra, ACT 0200 Australia firstname.lastname@example.com Tata Institute, Pune,More information
SUPPORT VECTOR MACHINE (SVM) is the optimal
130 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 1, JANUARY 2008 Multiclass Posterior Probability Support Vector Machines Mehmet Gönen, Ayşe Gönül Tanuğur, and Ethem Alpaydın, Senior Member, IEEEMore information
Data Mining - Evaluation of Classifiers
Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010More information
Chapter 6. The stacking ensemble approach
82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also describedMore information
A User s Guide to Support Vector Machines
A User s Guide to Support Vector Machines Asa Ben-Hur Department of Computer Science Colorado State University Jason Weston NEC Labs America Princeton, NJ 08540 USA Abstract The Support Vector MachineMore information
A Simple Introduction to Support Vector Machines
A Simple Introduction to Support Vector Machines Martin Law Lecture for CSE 802 Department of Computer Science and Engineering Michigan State University Outline A brief history of SVM Large-margin linearMore information
Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris
Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector MachinesMore information
Roulette Sampling for Cost-Sensitive Learning
Roulette Sampling for Cost-Sensitive Learning Victor S. Sheng and Charles X. Ling Department of Computer Science, University of Western Ontario, London, Ontario, Canada N6A 5B7 {ssheng,email@example.com information
STA 4273H: Statistical Machine Learning
STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! firstname.lastname@example.com! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification ConstructMore information
Supervised Learning (Big Data Analytics)
Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be usedMore information
Active Learning SVM for Blogs recommendation
Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find theMore information
Massive Data Classification via Unconstrained Support Vector Machines
Massive Data Classification via Unconstrained Support Vector Machines Olvi L. Mangasarian and Michael E. Thompson Computer Sciences Department University of Wisconsin 1210 West Dayton Street Madison, WIMore information
Learning with Local and Global Consistency
Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf Max Planck Institute for Biological Cybernetics, 7276 Tuebingen, GermanyMore information
Working Set Selection Using Second Order Information for Training Support Vector Machines
Journal of Machine Learning Research 6 (25) 889 98 Submitted 4/5; Revised /5; Published /5 Working Set Selection Using Second Order Information for Training Support Vector Machines Rong-En Fan Pai-HsuenMore information
Statistical Machine Learning
Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classesMore information
Learning with Local and Global Consistency
Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf Max Planck Institute for Biological Cybernetics, 7276 Tuebingen, GermanyMore information
Spam detection with data mining method:
Spam detection with data mining method: Ensemble learning with multiple SVM based classifiers to optimize generalization ability of email spam classification Keywords: ensemble learning, SVM classifier,More information
Support Vector Pruning with SortedVotes for Large-Scale Datasets
Support Vector Pruning with SortedVotes for Large-Scale Datasets Frerk Saxen, Konrad Doll and Ulrich Brunsmann University of Applied Sciences Aschaffenburg, Germany Email: {Frerk.Saxen, Konrad.Doll, firstname.lastname@example.com information
Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-6, January 2013 Artificial Neural Network, Decision Tree and Statistical Techniques Applied for DesigningMore information
Convex Optimization SVM s and Kernel Machines
Convex Optimization SVM s and Kernel Machines S.V.N. Vishy Vishwanathan firstname.lastname@example.com National ICT of Australia and Australian National University Thanks to Alex Smola and Stéphane Canu S.V.N.More information
Multiple Kernel Learning on the Limit Order Book
JMLR: Workshop and Conference Proceedings 11 (2010) 167 174 Workshop on Applications of Pattern Analysis Multiple Kernel Learning on the Limit Order Book Tristan Fletcher Zakria Hussain John Shawe-TaylorMore information
Robust Cost Sensitive Support Vector Machine
Shuichi Katsumata The University of Tokyo shuichi email@example.com Akiko Takeda The University of Tokyo firstname.lastname@example.com Abstract In this paper we consider robust classificationsMore information
Classifiers & Classification
Classifiers & Classification Forsyth & Ponce Computer Vision A Modern Approach chapter 22 Pattern Classification Duda, Hart and Stork School of Computer Science & Statistics Trinity College Dublin DublinMore information
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analyticalMore information
Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a SupportMore information
A Neural Support Vector Network Architecture with Adaptive Kernels. 1 Introduction. 2 Support Vector Machines and Motivations
A Neural Support Vector Network Architecture with Adaptive Kernels Pascal Vincent & Yoshua Bengio Département d informatique et recherche opérationnelle Université de Montréal C.P. 6128 Succ. Centre-Ville,More information
large-scale machine learning revisited Léon Bottou Microsoft Research (NYC)
large-scale machine learning revisited Léon Bottou Microsoft Research (NYC) 1 three frequent ideas in machine learning. independent and identically distributed data This experimental paradigm has drivenMore information
Online Classification on a Budget
Online Classification on a Budget Koby Crammer Computer Sci. & Eng. Hebrew University Jerusalem 91904, Israel firstname.lastname@example.com Jaz Kandola Royal Holloway, University of London Egham, UK firstname.lastname@example.com information
Classification of Bad Accounts in Credit Card Industry
Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In additionMore information
Several Views of Support Vector Machines
Several Views of Support Vector Machines Ryan M. Rifkin Honda Research Institute USA, Inc. Human Intention Understanding Group 2007 Tikhonov Regularization We are considering algorithms of the form minMore information
DUOL: A Double Updating Approach for Online Learning
: A Double Updating Approach for Online Learning Peilin Zhao School of Comp. Eng. Nanyang Tech. University Singapore 69798 email@example.com Steven C.H. Hoi School of Comp. Eng. Nanyang Tech. UniversityMore information
Maximum Margin Clustering
Maximum Margin Clustering Linli Xu James Neufeld Bryce Larson Dale Schuurmans University of Waterloo University of Alberta Abstract We propose a new method for clustering based on finding maximum marginMore information
Machine Learning in FX Carry Basket Prediction
Machine Learning in FX Carry Basket Prediction Tristan Fletcher, Fabian Redpath and Joe D Alessandro Abstract Artificial Neural Networks ANN), Support Vector Machines SVM) and Relevance Vector MachinesMore information
A Linear Programming Approach to Novelty Detection
A Linear Programming Approach to Novelty Detection Colin Campbell Dept. of Engineering Mathematics, Bristol University, Bristol Bristol, BS8 1 TR, United Kingdon C. email@example.com Kristin P. BennettMore information
Evaluation & Validation: Credibility: Evaluating what has been learned
Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? How can we evaluate a model Test the model Statistical tests Considerations in evaluating a ModelMore information
A Support Vector Method for Multivariate Performance Measures
Thorsten Joachims Cornell University, Dept. of Computer Science, 4153 Upson Hall, Ithaca, NY 14853 USA firstname.lastname@example.com Abstract This paper presents a Support Vector Method for optimizing multivariateMore information
1 SURVIVABILITY OF COMPLEX SYSTEM SUPPORT VECTOR MACHINE BASED APPROACH Y, HONG, N. GAUTAM, S. R. T. KUMARA, A. SURANA, H. GUPTA, S. LEE, V. NARAYANAN, H. THADAKAMALLA The Dept. of Industrial Engineering,More information
Tree based ensemble models regularization by convex optimization
Tree based ensemble models regularization by convex optimization Bertrand Cornélusse, Pierre Geurts and Louis Wehenkel Department of Electrical Engineering and Computer Science University of Liège B-4000More information
Transform-based Domain Adaptation for Big Data
Transform-based Domain Adaptation for Big Data Erik Rodner University of Jena Judy Hoffman Jeff Donahue Trevor Darrell Kate Saenko UMass Lowell Abstract Images seen during test time are often not fromMore information
On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers
On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers Francis R. Bach Computer Science Division University of California Berkeley, CA 9472 email@example.com AbstractMore information
Support Vector Machines
Support Vector Machines Charlie Frogner 1 MIT 2011 1 Slides mostly stolen from Ryan Rifkin (Google). Plan Regularization derivation of SVMs. Analyzing the SVM problem: optimization, duality. GeometricMore information
Overview. Evaluation Connectionist and Statistical Language Processing. Test and Validation Set. Training and Test Set
Overview Evaluation Connectionist and Statistical Language Processing Frank Keller firstname.lastname@example.com Computerlinguistik Universität des Saarlandes training set, validation set, test set holdout, stratificationMore information
Early defect identification of semiconductor processes using machine learning
STANFORD UNIVERISTY MACHINE LEARNING CS229 Early defect identification of semiconductor processes using machine learning Friday, December 16, 2011 Authors: Saul ROSA Anton VLADIMIROV Professor: Dr. AndrewMore information
WE DEFINE spam as an e-mail message that is unwanted basically
1048 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 5, SEPTEMBER 1999 Support Vector Machines for Spam Categorization Harris Drucker, Senior Member, IEEE, Donghui Wu, Student Member, IEEE, and VladimirMore information
Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011
Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learningMore information
The Set Covering Machine
Journal of Machine Learning Research 3 (2002) 723-746 Submitted 12/01; Published 12/02 The Set Covering Machine Mario Marchand School of Information Technology and Engineering University of Ottawa Ottawa,More information
Foundations of Machine Learning On-Line Learning. Mehryar Mohri Courant Institute and Google Research firstname.lastname@example.com
Foundations of Machine Learning On-Line Learning Mehryar Mohri Courant Institute and Google Research email@example.com Motivation PAC learning: distribution fixed over time (training and test). IID assumption.More information
Online Active Learning Methods for Fast Label-Efficient Spam Filtering
Online Active Learning Methods for Fast Label-Efficient Spam Filtering D. Sculley Department of Computer Science Tufts University, Medford, MA USA email@example.com ABSTRACT Active learning methodsMore information
Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval
Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!More information
Table 1: Summary of the settings and parameters employed by the additive PA algorithm for classification, regression, and uniclass.
Online Passive-Aggressive Algorithms Koby Crammer Ofer Dekel Shai Shalev-Shwartz Yoram Singer School of Computer Science & Engineering The Hebrew University, Jerusalem 91904, Israel {kobics,oferd,shais,email@example.com information
Behavior Analysis of SVM Based Spam Filtering Using Various Kernel Functions and Data Representations
ISSN: 2278-181 Vol. 2 Issue 9, September - 213 Behavior Analysis of SVM Based Spam Filtering Using Various Kernel Functions and Data Representations Author :Sushama Chouhan Author Affiliation: MTech ScholarMore information
(Quasi-)Newton methods
(Quasi-)Newton methods 1 Introduction 1.1 Newton method Newton method is a method to find the zeros of a differentiable non-linear function g, x such that g(x) = 0, where g : R n R n. Given a startingMore information
An Efficient Implementation of an Active Set Method for SVMs
Journal of Machine Learning Research 7 (2006) 2237-2257 Submitted 10/05; Revised 2/06; Published 10/06 An Efficient Implementation of an Active Set Method for SVMs Katya Scheinberg Mathematical ScienceMore information
A Study on SMO-type Decomposition Methods for Support Vector Machines
1 A Study on SMO-type Decomposition Methods for Support Vector Machines Pai-Hsuen Chen, Rong-En Fan, and Chih-Jen Lin Department of Computer Science, National Taiwan University, Taipei 106, Taiwan firstname.lastname@example.com information
Scalable Developments for Big Data Analytics in Remote Sensing
Scalable Developments for Big Data Analytics in Remote Sensing Federated Systems and Data Division Research Group High Productivity Data Processing Dr.-Ing. Morris Riedel et al. Research Group Leader,More information
Intrusion Detection via Machine Learning for SCADA System Protection
Intrusion Detection via Machine Learning for SCADA System Protection S.L.P. Yasakethu Department of Computing, University of Surrey, Guildford, GU2 7XH, UK. email@example.com J. Jiang DepartmentMore information
Adapting Codes and Embeddings for Polychotomies
Adapting Codes and Embeddings for Polychotomies Gunnar Rätsch, Alexander J. Smola RSISE, CSL, Machine Learning Group The Australian National University Canberra, 2 ACT, Australia Gunnar.Raetsch, Alex.SmolaMore information
Open-Set Face Recognition-based Visitor Interface System
Open-Set Face Recognition-based Visitor Interface System Hazım K. Ekenel, Lorant Szasz-Toth, and Rainer Stiefelhagen Computer Science Department, Universität Karlsruhe (TH) Am Fasanengarten 5, KarlsruheMore information
Linear Classification. Volker Tresp Summer 2015
Linear Classification Volker Tresp Summer 2015 1 Classification Classification is the central task of pattern recognition Sensors supply information about an object: to which class do the object belongMore information
UDC 004.75 A MACHINE LEARNING APPROACH TO SERVER-SIDE ANTI-SPAM E-MAIL FILTERING 1 2 I. Mashechkin, M. Petrovskiy, A. Rozinkin, S. Gerasimov Computer Science Department, Lomonosov Moscow State University,More information
Online Learning in Biometrics: A Case Study in Face Classifier Update
Online Learning in Biometrics: A Case Study in Face Classifier Update Richa Singh, Mayank Vatsa, Arun Ross, and Afzel Noore Abstract In large scale applications, hundreds of new subjects may be regularlyMore information
Gaussian Processes in Machine Learning
Gaussian Processes in Machine Learning Carl Edward Rasmussen Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany email@example.com WWW home page: http://www.tuebingen.mpg.de/ carlMore information
Decision Support Systems
Decision Support Systems 50 (2011) 602 613 Contents lists available at ScienceDirect Decision Support Systems journal homepage: www.elsevier.com/locate/dss Data mining for credit card fraud: A comparativeMore information
Density Level Detection is Classification
Density Level Detection is Classification Ingo Steinwart, Don Hush and Clint Scovel Modeling, Algorithms and Informatics Group, CCS-3 Los Alamos National Laboratory {ingo,dhush,firstname.lastname@example.com Abstract WeMore information
Lecture 2: The SVM classifier
Lecture 2: The SVM classifier C19 Machine Learning Hilary 2015 A. Zisserman Review of linear classifiers Linear separability Perceptron Support Vector Machine (SVM) classifier Wide margin Cost functionMore information
Case Study Report: Building and analyzing SVM ensembles with Bagging and AdaBoost on big data sets
Case Study Report: Building and analyzing SVM ensembles with Bagging and AdaBoost on big data sets Ricardo Ramos Guerra Jörg Stork Master in Automation and IT Faculty of Computer Science and EngineeringMore information
Introduction to Logistic Regression
OpenStax-CNX module: m42090 1 Introduction to Logistic Regression Dan Calderon This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 Abstract Gives introductionMore information
DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 email@example.com information
Equity forecast: Predicting long term stock price movement using machine learning
Equity forecast: Predicting long term stock price movement using machine learning Nikola Milosevic School of Computer Science, University of Manchester, UK email@example.com Abstract LongMore information
Impact of Boolean factorization as preprocessing methods for classification of Boolean data
Impact of Boolean factorization as preprocessing methods for classification of Boolean data Radim Belohlavek, Jan Outrata, Martin Trnecka Data Analysis and Modeling Lab (DAMOL) Dept. Computer Science,More information
Support Vector Clustering
Journal of Machine Learning Research 2 (2) 25-37 Submitted 3/4; Published 2/ Support Vector Clustering Asa Ben-Hur BIOwulf Technologies 23 Addison st. suite 2, Berkeley, CA 9474, USA David Horn SchoolMore information
C19 Machine Learning
C9 Machine Learning 8 Lectures Hilary Term 25 2 Tutorial Sheets A. Zisserman Overview: Supervised classification perceptron, support vector machine, loss functions, kernels, random forests, neural networksMore information
A Two-Pass Statistical Approach for Automatic Personalized Spam Filtering
A Two-Pass Statistical Approach for Automatic Personalized Spam Filtering Khurum Nazir Junejo, Mirza Muhammad Yousaf, and Asim Karim Dept. of Computer Science, Lahore University of Management SciencesMore information
SUPPORT vector machine (SVM) formulation of pattern
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 3, MAY 2006 671 A Geometric Approach to Support Vector Machine (SVM) Classification Michael E. Mavroforakis Sergios Theodoridis, Senior Member, IEEE AbstractMore information
Machine Learning in Spam Filtering
Machine Learning in Spam Filtering A Crash Course in ML Konstantin Tretyakov firstname.lastname@example.com Institute of Computer Science, University of Tartu Overview Spam is Evil ML for Spam Filtering: General Idea, Problems.More information
Mining the Software Change Repository of a Legacy Telephony System
Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,More information
Linear Threshold Units
Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linearMore information
The Artificial Prediction Market
The Artificial Prediction Market Adrian Barbu Department of Statistics Florida State University Joint work with Nathan Lay, Siemens Corporate Research 1 Overview Main Contributions A mathematical theoryMore information
Machine Learning Final Project Spam Email Filtering
Machine Learning Final Project Spam Email Filtering March 2013 Shahar Yifrah Guy Lev Table of Content 1. OVERVIEW... 3 2. DATASET... 3 2.1 SOURCE... 3 2.2 CREATION OF TRAINING AND TEST SETS... 4 2.3 FEATUREMore information


text: OMA grants 'push-to-communicate for public safety' license to 3GPP08 Apr 2015 10:58 AM Move will enable 3GPP to use the OMA PCPS specifications in its work to develop a standard for 4G LTE mission critical push-to-talk applications and thereby avoid 'reinventing the wheel'Open Mobile Alliance (OMA) has agreed to let the standards body 3GPP use its Push-to-Communicate for Public Safety (PCPS) specifications in its work to develop standard specifications for mission critical push-to-talk (MCPTT) on LTE. A copyright agreement is now in place.The agreement, approved by the OMA Board and now out for signature among the 3GPP SDOs, is a vital aid to the progress of the work of 3GPP's new SA6 working group, allowing them to use PCPS 1.0 texts where needed to complete 3GPP MCPTT without having to 're-invent the wheel'.In recognition of the efforts in both OMA and 3GPP to get this decision made and implemented in good time, the OMA general manager, Seth Newberry, commented: "OMA extends its thanks to all who worked so hard to forge this agreement and we wish SA6 the best of luck in developing the LTE MCPTT specification. We look forward to future collaboration with 3GPP and the first responder community the world over."Balazs Bertenyi, the 3GPP SA chairman, praised the way that the memberships of both bodies were able to move on this agreement. "This copyright agreement will ensure that the specifications for LTE MCPTT get done. If the stakeholders hadn't shown the goodwill and enthusiasm that has been in evidence, we could have been facing delays in getting some of the specifications based on OMA PoC ready on time."LTE lacks the kind of mission critical applications required by first responders on their communications networks. However, the two-way radio standards currently used for most mission critical networks around the world, such as TETRA, P25 and Tetrapol, are based on narrowband systems. While they have developed highly sophisticated voice applications, they are limited in the amount of data they can handle.The desire to harness more data applications, such as video streaming for use by first responders, requires broadband technology, so many countries see a move to LTE-based technology for future emergency services communication networks. However, mission critical standards have to be written for LTE first. 3GPP's SA6 working group is tasked with writing these standard specifications. James Atkinson EditorLinkedIn |Google + |Email Leave a CommentPlease write code to prove you're humanMore on... 3GVodafone UK to boost rural coverage with 100 3G community hubsThales offers 4G broadband MVNO for public security and enterprise customersCambridge Wireless celebrates 15 year anniversaryMore on... Critical CommunicationsFinland and Norway sign cross-border TETRA communication dealCC Europe 2017: Hytera showcasing new base station and handheld radioPublic safetyTCCA pinpoints key focus areas for LTE-based critical communicationsMotorola launches all-in-one single site TETRA digital radio system Southern Tyrol TETRA network gets a workout at Biathlon World Cup Latest in Mobile infrastructureQuortus and Expeto announce PaaS virtual LTE networks for enterprisesMWC17: Zyxel to debut new LTE and Wi-Fi indoor gatewayMWC17: Ericsson expands 5G access and transport portfolio


text:
Red & White Checked Afghan by Melissa Leapman
Welcome! You are browsing as a guestWould you like to see projects made from this pattern and much more? create a free account What am I missing?
Red & White Checked Afghan
This a free pattern from Michaels from Craftsmart yarn.
For more information, see: http://m.michaels.com/on/demandware.store/Sites-Mich...


text:
- Iris: Secrets - by Celestial-StarLightXWatch
©2012-2017 Celestial-StarLightX
It's Awesome and the color is perfect ^^
she's pretty...
I love it so much too! I love the colouring and shading of the hair, it's really awesome! I love her beautiful expression, it's just so pretty!
love the design, and ur character looks intresting
I think it looks great I love light colors so I think your coloring is great. X3 How you colored her gives her a bit of a feminine flare. ^ ^
Thanks, It's nice of you to compliment the colouring cause it's really what I'm working hard on now ^^
Wow, i love it. Well done!
Awesome dragoness, she looks really cute and I really like how the hair looks
Whoa so amazing


text:
It only takes a minute to get started. Fill out our simple sign up form and login to the site.
This step is optional but will drastically enhance your chances of receiving views from other daters on the website. Let people know a bit about yourself, your hobbies and upload any nice photographs of yourself.
Utilise our website's state-of-the-art search to spot members who meet your criteria. Or just look through listings and spot members that you find attractive.
Discovered someone who looks interesting? Send a letter to them or just a wink and see what comes of it. With a little luck you'll be on your way to a once in a lifetime experience.
It's quick and simple


text:
Pet Sitter in El Paso
Responsible college student with lots of experience with animals is willing to house or pet sit your furbabies. Several years experience with a variety of animals including cats, dogs, hedgehogs and turtles. Also willing to care for your house while you are out of town. References available.
Pet Sitter Details
|Email Address
|Phone Number
|501-733-... Confirmed
Dog Walking
Pet Feeding
Pet Sitting
Overnight Care
Notes made here will only be seen by you. If you want to message the owner of this listing
More in El Paso, Texas
Looking for Pet Sitter jobs in El Paso, TX?
Find available jobs in your city.


text:
Discussion in 'Off Topic' started by DRE, Mar 25, 2013.
What's the best over-the-counter solution for sinus headaches?
Not sure if you can get this in america, but I use this.
I'm not married or in any relationships. lol.
I don't remember if Sudafed works for me but I'll try it out thanks.
You're better off asking a doctor.
Xenforo's my doctor.
Wouldn't you be better off treating the allergies though? Take an anti-histamine.
For a Sinus Headache, I try to go with something that has Pseudoephedrine (congestion) and a pain reliever in it.
For allergies in general, Alavert is good as it is non-drowsy. Zrytec is a heavy hitter though for serious cases.
I also like:
Vicks vapo-rub -- this stuff is a god send
Vicks nasal spray -- I find this works really well at cleaning out my nasal passages
I've been in my house all day. This is a sinus headache. No sneezing, no runny nose, no cough, no itching, no watery eyes. Just headache and nose is stuffy, which is basically what Sudafed treats. I wonder what else does. Cause most of the allergy medicine I have in my cabinet doesn't treat that stuff.
That Pseudophedrine is Sudafed. Zyrtec is the bomb but it's expensive last time I checked.
I don't think I'll be able to update to Xenforo 1.1.4 tomorrow. Damn allergies.
stop eating at McDonald's.
I just got a headache after eating their french fries for 4 hours.
never happened before, but it happened today!
That is more than likely dehydration due to the amount of salt on them.
My advice is don't ask for medical advice from unqualified persons on the internet
Speak to a pharmacist or a doctor.
Go see a doctor. sinus headaches might be related to a sinus infection.
I have to take either Zyrtec or Sudafed every day of the year due to my allergies.
Wouldn't a couple of sachets of laxatives help with Sinus/headaches? I'm no doctor btw.
Stop by your local doctor's office. I've fought allergies for over a decade, and have tried everything.
Diet & Exercise does play a major role in a healthy immune system; riding a road bicycle helped me (instead of the motorcycle). Cut back on processed sugars, red meat, and sodas/alcohol (which is a challenge at some events).
Separate names with a comma.


text:
by Staff Writers
Washington (AFP) Jan 10, 2013
The United States announced Thursday that Australian police have arrested an alleged associate of the notorious jailed international arms trafficker Viktor Bout.
The US Drug Enforcement Administration said Richard Ammar Chichakli was arrested on Wednesday in Australia at the request of US authorities.
Bout, who was convicted by a US court in 2011 of conspiring to sell arms to Colombia's FARC rebels, has been accused over the past two decades of selling arms to despots embroiled in some of the world's bloodiest conflicts.
He was the inspiration for the arms smuggler played by Nicolas Cage in "Lord of War" (2005), and has been dubbed the "Merchant of Death."
His alleged associate Chichakli, who holds both Syrian and US citizenship, is charged with conspiring with Bout to try to purchase two aircraft from companies located in the United States and use them to ship arms.
This is alleged to have happened in 2007, a year before Bout's arrest in Thailand. Bout is now serving a 25-year jail term in the US.
Buying the planes would have violated a US executive order imposed first against Bout, and later against Chichakli, banning them from carrying out any transactions within the US.
The order, imposed in line with UN sanctions, stems from their links with former Liberian president Charles Taylor, convicted of war crimes last year for supporting for rebels in Sierra Leone in exchange for blood diamonds.
Chichakli is also charged with money laundering conspiracy, wire fraud conspiracy and six counts of wire fraud in connection with the attempted aircraft purchase.
"As alleged, Richard Ammar Chichakli consorted with the world's most notorious arms trafficker in the purchase of aircraft that would be used to transport weapons to some of the world's bloodiest conflict zones, in violation of international sanctions," Manhattan US Attorney Preet Bharara said in a statement issued by the DEA.
All told, Chichakli faces nine criminal counts, each carrying a maximum penalty of 20 years in jail.
Police in the Australia confirmed that a 53-year-old man had been arrested after he applied for a position with local police.
"He was identified as a person of interest through routine background checks as part of the application procedure," a spokeswoman for the police in the state of Victoria said.
"He had not been offered employment with Victoria Police or started any training," she added.
The spokeswoman would not comment on whether Chichakli had applied under his real name but said stringent background checks were carried out as a matter of course for all applicants to such jobs.
While not police officers, Protective Services Officers or PSOs are employed by Victoria Police and are often deployed to train stations to ensure safety for travelers.
Related Links
The Military Industrial Complex at SpaceWar.com
Learn about the Superpowers of the 21st Century at SpaceWar.com
|The content herein, unless otherwise known to be public domain, are Copyright 1995-2014 - Space Media Network. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA Portal Reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. Advertising does not imply endorsement,agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. Privacy Statement


text:
Recent Posts
- Health, Hope, and Mud-Dung Candy: Living in the Present January 22, 2017
- Notebook 1: God's Will & Interpreting History January 4, 2017
- "In the beginning was the Word" and we took it very seriously. October 20, 2016
- Porpoises and Purposes September 11, 2016
- Remembering Dad January 18, 2016
Recent Comments
lupiecat95 on Health, Hope, and Mud-Dung Can… K. C. Dockal on Notebook 1: God's Will & I… Tim Miller on Notebook 1: God's Will & I… K. C. Dockal on "In the beginning was th… Steve Brown on "In the beginning was th…
Posts I Like
Tag Archives: randomness
Emergency Salads, Tornadoes, and Permanent Cow Fixtures
I realized I cannot stand up and be who and what I need to be without first kneeling and being humble to what I have been given. Continue reading
Posted in faith Tagged death, faith, family, fragility, life, randomness, time, tornadoes 2 Comments


text:
narrowing it down
Today my parents and I went out to try to find a rifle for me. My Dad said I should probably get a rifle as he is familiar with them. I wanted a handgun but Dad said I wasn't ready for one yet. So we set out to Gander Mountain to take a look at what they had. Dad said I should stick with a .22 for now as he doesn't want me to hurt my shoulder with a shotgun. I have a pretty low pain tolerance so I think it's probably a good idea to stick with a .22, as my Dad suggested. He's always looking out for me, as good parents do for their children. ;)
What we liked best was the Ruger 10 + 1 22 Long Rifle Heavy Stainless Barrel & Laminate Stock because my Dad and I figured for that price, it has to be a good gun. Does anyone have any experience with this gun?
We only want to target shoot with the gun we buy as my Mom said I would probably feel bad if I ever shot any animals. I have a couple of toy poodles that would probably be upset with me if I ever brought home another animal, dead or alive. :D
Anyway, my Dad and I are interested in what you all think. My fellow doctor friends want to go shooting with me so I hope to make a decision and purchase soon.
I am very excited to hold such a powerful weapon in my hands.
I have to go to sleep now. I'm up late and I'm trying to be so quiet so as not to wake my parents. We go to church pretty early on Sunday mornings so I better get some sleep.
Thats a nice bolt action but you would pay a lot less for a Ruger 10/22 semi auto rifle. They are alot of fun and you can either plink with the standard 10 round magazine or go get a 30 round magazine.
Not sure you would be able to handle the power of the Ruger in .22lr. I would suggest the Daisy Red Ryder for starters and work up to the Airsoft G5 clone...then a .22.
ditto on the comment about "growing a pair." No offense or anything, but I'm assuming you are of a pretty significant age if you are a dermatologist. Now go choose the gun you want and let mom and dad go about their own business.
This thread has to be a joke.
[QUOTE=frankie331978;543613] I have a pretty low pain tolerance so I think it's probably a good idea to stick with a .22,
I am very excited to hold such a powerful weapon in my hands.
Im sorry i think i had something crazy in my eyes, powerful?:D?
Im sorry man but a .22 in my book is in the same catagory as a air gun. When you stub your toe do you go to the ER? guns are guns man, grow a pair, a shotgun weighs 8 lbs, how much do you weigh? dont let a gun kick your ass. Im sorry but i agree, this has to be a joke, your a doctor and live with mom and dad? My first gun ever was a Mossberg 20ga back when i was 19 and the first time i shot it i sold it the next day, it was a lot weaker then i thought so i went to a 12 GA and i have been in love since then.
If this is a real post here what i suggest, first off never refer to a .22 as such a powerful gun, thats just crazy..and next, you should get your own place and start running your life without mom and dad, next go out and look at a nice Glock or 1911, hell if you can man up maybe a 20 GA (feels like a .22 anyway) but if you want a .22 the ruger is a nice rifle but choose a gun that you like not what mom and dad think is best, hell if you like .22 so much look into a AR-15, more powder but almost no recoil at all, but like i said i have a hard time believing this is a legit post.
I'm a 'girl' and I shot a 20 guage shotgun and I thought it hurt. LOL I also shot Bear's Quigly and that hurt even worse. I'll stick with handguns and rifles........other than that darned Quigly.
O ya, if your looking for a .22 get one you can have fun with, i recomend a savage stevens arms 22lr, semi auto, its fun and only cost around $150.
Here is a quick vid of mine.
|All times are GMT. The time now is 02:46 AM.
Copyright ©2000 - 2017, Jelsoft Enterprises Ltd.


text:
Posted on September 4, 2015 by: acid stag
Sweden's Summer Heart shares new single just ahead of his USA/EUR tour
We claim no credit for any images, music and/or videos posted on this site unless otherwise noted. All audio and visual content is copyright to its respectful owners. We are also in no way responsible for, or have control of the content of any external web site links. This website was created as a promotional tool. Please support the artists and purchase their work.


text:
This best-selling chandelier by the Jamie Young Company is meticulously hand-made. Cut capiz shells are carefully shaped into a luminous graphic pattern on the dark ground backing made of hard resin. Easily worked into coastal or urban environments, its striking style and coordinating chain will add instant luxury to your decor.
With over 30 years of combined experience, the Jamie Young Company offers designed products and manufacturing expertise. Our selection of contemporary Jamie Young table lamps blurs the line between engineering and art. Enhance the look of your modern home décor with a beautiful Jamie Young table lamp.
Our team has over 100 years combined experience with contemporary lighting.
Every EuroStyleLighting.com order is backed by our 100% Satisfaction Pledge.
Shipping and Delivery Information - For item EU7C797 if ordered by 1 pm Pacific Time


text:
From staff and wire reports
According to the Sports Business Journal, Major League Baseball Advanced Media and the Major League Baseball Players' Association have filed a petition for a writ of certiorari to the U.S. Supreme Court, seeking to overturn lower court rulings that allowed a fantasy baseball company — CDM Fantasy Sports — to use players' names and statistics without a licensing fee.
The 41-page filing was docketed Feb. 28 and CDM has until March 27 to respond, if it chooses.
Using First Amendment and right of publicity arguments, MLBAM and the union have argued that players should be paid when their names are used for fantasy baseball leagues, in the same way players are paid when their names are used to endorse products.
But the lower courts found that fantasy leagues' broad use of statistics isn't the same as faking an endorsement from a player and not paying him.
Big media companies such as Yahoo, ESPN and CBS operate online fantasy leagues. Fantasy league participants create teams comprised of real MLB players. Over the course of a season, participants track statistics to judge how well players are performing.
According to the Journal, MLBAM and the union are hoping to improve their chances of getting the case heard by the High Court by arguing to the broader need of a uniform standard for right of publicity disputes.
Conversation guidelines: USA TODAY welcomes your thoughts, stories and information related to this article. Please stay on topic and be respectful of others. Keep the conversation appropriate for interested readers across the map.


text:
Seann William Scott was born in Cottage Grove, Minnesota, to Patricia Anne (Simons) and William Frank Scott, a factory worker. He was discovered at a talent competition in Los Angeles, and almost immediately was flown to New York by ABC to test for "All My Children". His face has also been seen from his basketball-playing appearance on a national Sunny Delight commercial and the recent American Express campaign with Magic Johnson.
Seann was also seen on "Something So Right" for ABC. Seann shot a lead role in Aerosmith's music video, "Hole in My Soul" from the "Nine Lives" CD. Previously, Seann has worked with Mark-Paul Gosselaar (of "Saved by the Bell") and Talia Shire ("Rocky") on the NBC movie of the week, "Born Into Exile", and has had a recurring role in the Warner Brothers TV series, "Unhappily Ever After", as well as appearing on "Sweet Valley High".
Name: Seann William Scott
Known for: Role Models
BirthDay: 03, Oct ,1976


text:
HALIFAX - The aunt of Dustan Preeper said Wednesday she was not surprised at his outburst in court because he has Asperger "tendencies," and he didn't go looking for trouble in the case of Ben Hare.
When asked if he wanted to say anything before the judge delivered his sentence for two counts of murder against Melissa Peacock and Ben Hare, Preeper said "you can all go f--- yourselves" and gave a finger to the gallery.
Tiny White, Preeper's aunt, told reporters outside court that Preeper has "tendencies of Aspergers" and attention deficit disorder (ADD).
"Does it surprise me that he said anything? Not at all," she said.
When asked if the court appearance was difficult for their family, White choked back tears and nodded before saying, "it is."
In Supreme Court, the agreed statement of facts read that Hare attacked Dustan Preeper on July 7, 2010 outside a Truro apartment building where Preeper was outside drinking with friends, and stabbed him in the face and back.
During the ensuing fight with both men's groups of friends, Preeper obtained an aluminum baseball bat and struck Hare with it, who fell to the ground unconscious.
The court heard Preeper chased others off the property then returned to hit Hare multiple times in the head and chest while he lay prone on the ground.
White said the whole incident wouldn't have happened if Hare had not initiated the assault, and said she was told at the hospital Preeper wouldn't have survived if the wounds had been another "eighth of an inch."
"Dusty didn't go looking for the trouble," she said. "He is not a monster."
Haley Ryan – TC Media


text:
Character Cake Help
Decorating By diamondsmom Updated 21 May 2009 , 1:57am by cricket0616
Hi CC family,
I'm in need of a Buttercream recipe for character cakes. I did an elmo and it turned out horrible... it was too soft then it it too hard.. So i gave up.
Is it better to use a crusting buttercream or not for the Character cake.
HELP! My son's birthday is comingup and I don't want to create another mess..
Thanks Diamondsmom
have you tried the IMBC? It's great to pipe with and stays soft.
I have found the Wilton class buttercream recipe to work the best for character cakes for me. I substitute half of the crisco for butter and it still works great.
I agree with cherrycakes and use wilton BC recipe. I do the same thing and substitue half the crisco for butter and it works great. I think the full crisco is a little on the greasy side.
Quote by @%username% on %date%


text:
Charlie Sheen: 'No Lap Dances for Me, Thanks!'
Given his history, it was easy to believe reports earlier this week that Charlie Sheen had run off from his wife to go on a lap-dance binge in Las Vegas.
But the Two and a Half Men star's rep is vehemently denying those reports, according to Us Magazine.
Earlier accounts claimed that Sheen and his wife Brooke Mueller—who's currently pregnant with twins—had been "fighting relentlessly." In response, Sheen supposedly ran off on a strip-club binge at Vegas club Spearmint Rhino late last month with Spin City costar Michael Boatman.
Sheen's rep counters that Star Magazine, which originally reported the story, "Never lets the facts get in the way of good hatchet job. Had the reporter bothered to check, he or she would have learned that Charlie was in Vegas for a contractual appearance on behalf of Planet Hollywood's Prive."
Well, there it is; sorry to rub you the wrong way, Charlie.


text:
- Rebaptism
- Rebaptism \Re*bap"tism\ (r[=e]*b[a^]p"t[i^]z'm), n. A second baptism. [1913 Webster]
The Collaborative International Dictionary of English. 2000.
The Collaborative International Dictionary of English. 2000.
Rebaptism — may refer to: * Anabaptism, from Anabaptists ( re baptizers , from Greek ανα and βαπτιζω ) * Rebaptism (Latter Day Saints), associated to the Latter Day Saints * Believer s baptism, associated with Baptist traditions … Wikipedia
rebaptism — n. * * * … Universalium
rebaptism — noun A second or subsequent baptism; the act or ceremony of rebaptizing … Wiktionary
rebaptism — re·baptism … English syllables
rebaptism — Перекрещение … Вестминстерский словарь теологических терминов
rebaptism — "+ noun Etymology: re + baptism : a second baptism … Useful english dictionary
Rebaptism (Latter Day Saints) — Rebaptism is a practice in some denominations of the Latter Day Saint or Mormonism movement.Rebaptism began with the Anabaptists in 1529, during the time of the reformer/theologian Martin Luther and this practise is continued by denominations… … Wikipedia
Diabolical Rebaptism — refers to the rebaptism of an individual in the name of the devil. It was a common accusation made against the accused at witch trials, but seems to have been essentially the invention of demonologists.[1] References ^ Eradicating the Devil s… … Wikipedia
Baptism — • One of the Seven Sacraments of the Christian Church; frequently called the first sacrament , the door of the sacraments , and the door of the Church Catholic Encyclopedia. Kevin Knight. 2006. Baptism Baptism … Catholic encyclopedia
Theology of Huldrych Zwingli — The study of the theology of Huldrych Zwingli since the 1990s has been facilitated by a modern critical edition of his works. [ Huldreich Zwinglis Sämtliche Werke , Vols. I XIV, Zürich: Theologisher Verlag. The original publication was in the… … Wikipedia


text:
Drake Make Me Proud
October 14, 2011 by staff
Drake Make Me Proud, Drake Appearance of New York station Hot 97 radio "In Flex We Trust" on Thursday night was all about the women in his life.
The rapper took the opportunity to debut new single "Make Me Proud" which includes a verse from Young Money label mate Nicki Minaj.
However, pressured by DJ Funkmaster Flex, Drake said he is dating professional tennis player Serena Williams – and former flame Rihanna, saying that he's now just "super good friends" with the singer.
"I think she has their own things going on," said DJ Funkmaster Flex. "I have not caught her in a while."
As for Williams, who was discovered in the U.S. Open in August, said playing tennis is "good exercise", but denied that his recent call for "booty-popping the tape" was shot in his home.
"Wood is actually a nut in my apartment," he laughed.
The "Marvin's Room" also weighed in rapper Lil Wayne's mentor and the recent meat Jay-Z, which took place in their respective tracks "good" or "HAM"
"I just do my verse in rhythm," Drake said "It's good," a track from Lil Wayne "Tha Carter IV". "I am a soldier from Wayne, who put me here, so I did what I had to do and what you do with the song after that is entirely up to him … I think Wayne, who is never afraid to step to the the plate, did what he did back and that's what hip-hop is about. I have the utmost respect for Jay, is a brother to me. "
(Hov was played outside of the meat in the Shawn Carter Foundation Make the ordinary extraordinary fundraising in September, telling MTV News: "This is sport. That's the rap music.")
Drake and played down his own body potential, a freestyle Clipse Pusha-T of online rumored to be for the young rapper.
"I listened and I could not choose, I could not choose where I was supposed to be offended," said Drake. "I'm a fan of the Clipse. If it's addressed to me, that is a little more direct next time."
Drake will have the opportunity to take photos of their own with the release of second album "Take Care", on November 15 at Cash Money Records / Young Money Entertainment Records / Universal.
Please feel free to send if you have any questions regarding this post , you can contact on
Disclaimer: The views expressed on this site are that of the authors and not necessarily that of U.S.S.POST.


text:
'Charter 91' Seeks to Unite Iranians Around a Tolerant Vision
Author: Barbara Slavin Posted October 9, 2012
Inspired by the successful liberation movements of Eastern Europe and South Africa, a group of Iranian exiles including an Oscar-nominated actress has launched a new initiative to unite Iranians inside and outside Iran around a tolerant, non-violent vision of their country's future.
Read More: http://www.al-monitor.com/pulse/originals/2012/al-monitor/irancharter91.html
Barbara Slavin is a columnist for Al-Monitor and director of the Future of Iran Initiative at the Atlantic Council. On Twitter: @BarbaraSlavin1
Translate with Google


text:
Green Brick
The ads appeared on four consecutive pages. LEGO is a company that has fostered imagination, invention and creativity for over 60 years. So it is unusual for these ads to feature only long copy with minimal imagery. However, upon reading each of these scenarios the ad comes to life in a way that is unique only to the reader and how they see these playtime scenarios in their mind's eye. Typographic elements of kerning contrasted with tracking allow the reader to almost get lost in the copy selecting keywords for their imagination. The fourth ad in the series, "Yellow Brick" features a notepad with the tagline "Every LEGO brick tells a story. Build yours."
Creative Director / Copywriter: Aricio Fortes
Creative Director / Art Director: Paulo Coelho
Account Executive: Lo Braz
Illustrator: Eduardo Gomes
I absolutely love the idea but in terms of the little story, it could have been written heaps better.
What Jaap wrote. Nice concept and absolutely lovely art direction. Plus I really love that there's no logo.
Yeah that little green dinosaur is really cute, captures your imagination, too bad he was caught in a really weird nightmare.
Excellent concept though.
Even though I love the minimalist layout, I have to ask: 'Would kids really read all this?'
Target audience: parents. usu. adults.
message: you can utilize your kid's imagination instead of falling into existing patterns.
you can read more views here:
http://adsoftheworld.com/media/print/lego_red_brick under username: IsraelMallett or me, for eg.
I say - you and this IsraelMallett wouldn't by any chance be the same person?
Use the lego block within the copy to suggest an element of the story. Just do SOMETHING.
The Jobless Writer
Will Think for Salary
Popular Reads
Clio Sports
"Say Less, Do More": Words of Inspiration From the 2017 Clio Sports AwardsRead More
Inside the Agency
How DDB's Ari Weiss And Keith Reinhard Are Embracing Ad Legend Bill Bernbach's "Rebel Spirit"Read More
Clio Health
Ward+Robes Designer Gowns Offer Individuality for Hospitalized TeensRead More
Clio Awards
Empathy, Observation, Openness: IDEO's Roshi Givechi on the Narrative of Product DesignRead More


text:
Downloadable copy of Henrik Soderstrom's CV
- Rochester Institute of Technology, Rochester, NY: MFA, 2011
- Rhode Island School of Design, Providence, RI: BFA, 2008, graduated with honors
- Rhode Island School of Design, Providence, RI: concentration in History, Philosophy, & Social Science
- NyckelviksSkolan, Stockholm, Sweden: One-Year Art, Craft, and Design Program, 2004
Exhibitions & Awards
Cross-Disciplinary and Collaborative
- Attacca Quartet: East Alabama Arts Association, Auburn, AL, 2014: Designed & created projection imagery for the stage during performance of works by John Adams.
- FuturPointe Dance: Fringe Festival, Rochester, NY, 2013: Created projected imagery for a live performance.
- Respond: The Yards, Rochester, NY, 2012: Collaborated with sound artists to create a multidisciplinary performance curated by John Lake.
- Hunting Stories: Rochester, NY, 2012: Created album artwork released with new single.
- Society for Freshwater Science: RISD Nature Lab / RI Convention Center, 2011, Worked with environmental researchers to exhibit artwork at a national conference on sustainability and resource management.
- Music of Difference: 2011: Generated album artwork and related designs for the first Music of Difference recording featuring music by Robert J. Bradshaw, Aaron Alon, and Angelique Poteat.
- Music of Difference: The Kennedy Center, Washington, DC, 2010: Collaborated with musicians and composers to bring a multidisciplinary program.
- National Technical Institute for the Deaf: Sleeping Beauty: 2010: Collaborated with students and faculty to design and construct sets and props for a multimedia production choreographed by Thomas Warfield.
- Boyd/Goluses Duo- Visual Art Director: 2010: Produced graphic layout and managed visuals for the recording Quicksilver: Songs and Dances of the Americas by Bonita Boyd & Nicholas Goluses.
- Yo Yo Ma's Silk Road Project: Lincoln Center, New York, New York, 2009: Designed & created the multi-media set for the premier of the opera Layla & Majnun. (Also gave related lectures at RISD and SUNY Binghamton in March of 2009).
- Yo Yo Ma's Silk Road Project: Visual Art Director: 2009: Managed the creation of a 32-page booklet and related designs for the album Off the Map, which was nominated for a Grammy in 2010.
- The Knights Orchestra: Visual Art Director, New York, New York, 2009: Created a visual identity for The Knights Orchestra for use in web and print media.
- Silk Road Project: RISD, 2008, Worked with a collaborative studio to create an multi-disciplinary storytelling performance in collaboration with Ben Haggarty and the musicians of the Silk Road under the direction of Yo-Yo Ma.
- D.C.Y.F. Mural Project: Providence, RI, 2006: Led a group of students in the creation of a mural at DCYF.
- Collaboration with Yuri Kobayashi: Snowmass, CO, 2008: Created a collaborative sculpture for auction at Anderson Ranch Arts Center in Colorado.
- Performance Project with P.O.I. Stockholm Sweden, 2004: generated paintings as part of a multimedia performance involving musicians and video artists.
Visual Arts
- That's Not a Toy Ladder. Climb Up Here. Davison Gallery, Rochester, NY, 2016 (solo show)
- 17th Annual Juried Exhibition: Jan Dempsey Arts Center, Auburn, AL, 2015
- Spring Invitational: Jan Dempsey Arts Center, Auburn, AL, 2015 (Duo show with Nancy Green)
- Build It!: IDEA Museum, Mesa, Arizona, 2015 (invitational group)
- Auburn CityFest: Auburn, Alabama, 2015 (juried)
- Diptych: Jan Dempsey Arts Center, Auburn, AL, 2014 (invitational group)
- Honeywell Center: Wabash, Indiana, 2014 (juried)
- Works on Paper: Upstream Gallery, Kings Ferry, New York, 2013 (juried)
- Elves Dancing on the Water: Curated by Benjamin Woelk, Rochester, NY, 2012
- Design on the ROCs: Hungerford Building, Rochester, NY, 2011
- Response: Axiom Gallery, Rochester, NY, 2011 (collaboration with Hannah Lightbody)
- MFA Thesis Exhibition: Bevier Gallery, Rochester, NY, 2011
- Graduate Student Invitational: Liberty Hill Gallery, Rochester, NY, 2011 (invitational)
- ArtFest at the Museum of the Earth: Ithaca, NY, 2010 (juried)
- Put a 12-Foot Bird On It: Clarence, NY, 2010: Created a mural covering the interior of USA Bird Supply's 2,000 square foot showcase store.
- Paper, Wood & String: Dean's Gallery at RIT, Rochester, NY, 2010 (juried)
- The 25 Show: Rochester, NY, 2009, "Best in Show" (juried)
- Homes: Cairo Gallery, Seattle, Washington, 2009 (juried)
- Trio: Gallery at Aviv, Rochester, NY, 2009 (invitational)
- Functional Art: Gallery r, Rochester, NY, 2009 (juried)
- Milan International Furniture Fair: Milan, Italy, 2008 (juried by RISD and Lothar Windels)
- RISD Senior Exhibition: Woods Gerry Gallery, 2008
- Fall Exhibition: RISD Woods Gerry Gallery, 2007
- Testimony: Carr Haus Gallery, RISD, 2007 (juried)
- Glory: Red Door Gallery, RISD, 2006 (juried)
- RISD Foundations Exhibition: RISD Foundations Gallery Space, Providence, RI, 2004 (juried)
- NyckelviksSkolan Annual Exhibition: NyckelviksSkolan Gallery, Stockholm, Sweden, 2004 (juried)
- 2-D: drawing, drafting, oil painting, traditional egg tempera, fresco, ground preparation techniques, metal leafing, silk screening, etching, lithography, drypoint & non-toxic intaglio printmaking, wood block & lino-cut print making, letterpress, embossing, paper making.
- 3-D: woodworking, welding, soldering & metalsmithing, sand & ceramic shell casting, bookbinding & traditional paper craft, ceramics (turning, hand building, glazing), basic loom operation, upholstery, felting, plaster casting, stained glass.
- Computer: Mac: Adobe Photoshop & InDesign, MS Word, Graphite, Cobalt.
- Language: conversational Swedish, excellent English, editing & writing
- Roger Tory Peterson Institute of Natural History, Ithaca, NY, 2010: Created and exhibited paintings related to environmental sustainability, natural cycles, and the permanent collection at RTPI.
- The Julia and David White Artists' Colony / Rhode Island School of Design, San Jose, Costa Rica, 2009: Invited by RISD to be an artist-in-residence and visiting critic for a travel course led by Elinore Hollinshead.
- StoneCry, Atglen, PA, 2008-2009: Collaborated as a designer, builder, and idea-generator for the creation of an artist-residency program which encouraged the integration of faith, philosophy, and studio practice under the direction of Mark Potter.
- I-Park Artist's Enclave, East Haddam CT, 2008: Created works for the enclaves collection as an Artist-in-Residence in an international & multi-disciplinary program.
- Black Mountain Anthology, 2013: Writing and photo-documentation of Porch exhibition (curated by Tara Rochford and Henrik Soderstrom) published in a two-volume anthology by the North Carolina Arts Council and the United Arts Council
- The New York Times, February 27, 2009: For the Love of Layla: Review of collaboration with the Silk Road Project
- Providence Business News, 2009: RISD graduate designs set for Silk Road performance
- Esquire Magazine, 2008: The Future of Design: Featured with Naushon Hale and Da Sul Kim in an article on design innovation.
- Milan Design Week 2008 by Jacquelyn Davis
- Pantone Canvas, 2015: new work featured by Pantone
- RISD Portfolios, 2013: new work featured by RISD
- The Silk Road Project, Postcards from the Road, 2009: Article and image about multimedia Layla and Majnun design
- RISD Faculty Award for Consistent Excellence: 2008: For a student who demonstrates consistent excellence in their three years in the Department of Furniture Design
- RISD Department of Furniture Design Community Service Award: 2008: This award goes to a student who shows concern for the community and a willingness to serve the needs of others.
- Brooks Fellowship: Anderson Ranch Arts Center, 2008
- Ken Dresser '61 Scholarship: Rhode Island School of Design, 2005
Teaching Experience
- Indiana Wesleyan University: Marion, Indiana, 2011-present: Assistant Professor of Art & Design
- Visual Arts Workshop for Children, Auburn Arts Association, Auburn AL, 2015: Designed and led classes for children ages 4-6 and 7-12.
- Rhode Island School of Design Travel Course- Guest Critic: San-Jose, Costa Rica, 2009: Facilitated critiques and studio sessions with undergraduate students as a guest critic and artist-in residence.
- Rochester Institute of Technology: Teaching Assistantship with Prof. Elizabeth Kronfield, 2010-2011: Worked individually with sophomore Fine Arts students to integrate conceptual thinking with formal issues. Helped to facilitate group critiques.
- Rochester Institute of Technology: Alternate Teacher for Professors Bill Wolf, Sarah Sutton, Elizabeth Kronfield, & Eileen Bushnell, 2009-2011: Taught Foundations studios.
- Rhode Island School of Design: Teaching Assistantship with Prof. Elinore Hollinshead: 2005-2008: Worked with Foundations drawing students to integrate conceptual problem solving with technical development. Facilitated group critiques.
- Rhode Island School of Design: Teaching Assistantship with Prof. Joshua Enck, 2008: Worked with Foundations students individually and in group critiques to expand formal, spatial, and structural understanding.
- Rhode Island School of Design: Teaching Assistantship with Prof. Merlin Szasz, 2007: Worked with Foundations students individually and in groups critiques to explore formal issues.
- Rhode Island School of Design: Teaching Assistantship with Prof. Randy Willier, 2006: Worked with Foundation drawing students individually and in group critiques to develop understanding of perspective and formal issues. Helped to facilitate group critiques and led figure drawing sessions.
- Anderson Alley Studios: Rochester, NY, 2004: Teaching Assistant: Helped to foster a creative environment for 12-16 year olds to grow through art therapy.
University Service
- Lilly Fellows Program: Indiana Wesleyan University, Summer 2015-Summer 2018: Served as one of two faculty representatives for the University.
- Search Committee: Indiana Wesleyan University, Spring 2012-Spring 2014: Several search committees for the Division of Art.
- Space Planning Committee: Indiana Wesleyan University, Fall 2013-Spring 2014: Met with a committee of faculty and administrators to develop a proposal for the use of Center Hall involving writing and floor plan development.
- Secret Garden set painting: Indiana Wesleyan University, Spring 2013: Guided a team of students to produce the set painting for The Secret Garden.
- Affordable Housing Mural: Marion Indiana, 2012-13: Guided a team of IWU students to create a two-room mural at Affordable Housing's new community space with input from neighborhood members.
- Art Theme Housing Planning Committee: Indiana Wesleyan University, 2012-13: Met with a committee of faculty and Residence Life leaders to plan and offer housing options for art students to live together and serve the community with their creative work.
- Mikado set painting: Indiana Wesleyan University, Spring 2012: Guided a team of students to produce the set painting for Mikado.
Leadership Experience
- Director: Beard Arts Center Gallery and Visiting Artist Program, Marion, Indiana, 2012-present
- Executive Director: Gallery r, Rochester, NY 2010-2011: Orchestrated the efforts of RIT students, alumni, and faculty to run the school's metro showcase. Managed the gallery through a move to a new space.
- Special Events Coordinator: Gallery r, Rochester, NY 2009-2010: Worked with the faculty and administration of RIT and with the local community to organize events, including a benefit auction for Gallery r.
- RISD Academic Planning Committee, Undergraduate Representative: 2007-2008: Represented RISD's undergraduate student body to a faculty committee on issues of curriculum planning, student exhibitions, and collaborative degree programs with Brown University.
- Porch Collaborative: Rochester, NY, 2010: Co-curated and organized a multi-disciplinary show integrating the work of musicians, writers, and visual artists. The exhibit was coordinated in collaboration with the Black Mountain North Symposium, a national conference celebrating collaborative and experimental creativity.
- Morphology and Synergetics Conference: RISD, 2008: Helped to encourage cross-disciplinary thinking in the student body through the coordination of lectures, discussions, and collaborations.
- Resident Assistant: RISD, 2005-2007: Fostered the growth of community through dorm functions and campus wide events. Created an on-campus gallery in the freshman dorms.
- RISD Student Alliance Representative for the Department of Furniture Design: 2006-2008: Represented the department on issues of exhibition space, funding, and collaboration with other majors.
- Art For Food: RISD, 2005-2008: Led a team of RISD students to coordinate the creation and auctioning of artworks at the Gallery at 17 Peck to benefit Heifer International.
Lectures & Interviews
- Jules Collins Smith Museum of Art, Auburn AL, 2015: Presentation on studio practice.
- The Writer's Block, Auburn, AL, 2015: Interview with Phineas Dowling for WEGL and The Writer's Block podcast, produced by the Miller Writing Center.
- Studio 222, Auburn, AL, 2015: Presentation on process and concept development.
- RISD Career Services, Providence, RI, 2009: One of four alumni invited to present work and discuss experiences in the art & design field to current students.
- Yo-Yo Ma's Silk Road Ensemble, RISD, 2009: Gave an artist's slide lecture and demonstration to illuminate the conceptual and technical processes behind the set design for Laya & Majnun, which opened at New York's Lincoln Center in 2009.
- RISD Department of Furniture Design Alumni Presentation, Providence, RI, 2010: Invited to present recent works and critique senior design students in a studio setting.
- RIT Foundations Artist's Talk, Rochester, NY, 2011: Showed recent works and discussed concepts of obscuring clarity to first year students in a studio setting.
- Harley Alumni Talk, Chatterbox Club, Rochester, NY, 2010: One of five alumni selected to present recent works and philosophy of learning at a fundraising gala.
- RIT Foundations Art History Presentation, Rochester, NY, 2010: Slide lecture on the life, work, and philosophy of Robert Rauschenberg to art students.