text: 000 FXUS63 KSGF 250447 AFDSGF AREA FORECAST DISCUSSION NATIONAL WEATHER SERVICE SPRINGFIELD MO 1147 PM CDT Tue Mar 24 2015 .UPDATE... Issued at 1140 PM CDT TUE MAR 24 2015 All convection has ended across the Missouri Ozarks and southeastern Kansas for tonight. There will be brief break in the weather tonight before more active weather returns Wednesday. Near term forecast has been updated to reflect precipitation and sky cover trends. && .SHORT TERM...(Tonight) Issued at 141 PM CDT TUE MAR 24 2015 In the near term severe weather chances are the obvious concern. Sfc low over just north of KEQA in se KS as of 18z will progress east with a trailing dry line/sfc trough moving into the eastern cwfa by 21z. Cap in place now, but a combo of daytime warming, mid level cooling with increased lift and a narrow corridor of low level moisture advection is expected to weaken the cap over the next couple of hours allowing sfc convergence near the boundary to initiate storms. Latest high res guidance (HRRR, among others) have been consistent with developing convection by 21z-22z close to the se KS/sw Mo state line. While model dew points may be a bit bullish, still looks like a good bet with a corridor/broken line of convection moving quickly w-e through the cwfa late this afternoon and early this evening. ML CAPE values of 1000-2000 j/kg indicated by the HRRR in a narrow corridor along the sfc trough coupled with more than adequate deep level 0-6 km bulk shear of around 40-50 kts and 0-3 km helicity of 200-300 m2/s2 will allow rotating updrafts to develop with stronger convection. With somewhat limited low level moisture/higher cloud bases, the main storm hazard will be hail given steep mid level lapse rates. Localized severe winds also a possibility. The tornado risk would appear low (but not zero), again given the lack of deeper low level moisture and a veered low level sfc wind profile along the sfc trough. The severe window should be small time-wise. Storms should quickly move east through the cwfa this evening, weakening late with more limited instability. .LONG TERM...(Wednesday through Tuesday) Issued at 215 PM CDT TUE MAR 24 2015 A second shortwave, more positively tilted, will make it`s way through the region Wed with a cold frontal passage expected. Weather hazards may include some severe wx and a risk of excessive rainfall over the southeast half of the cwfa. Storms are expected to re-initiate in the afternoon gradually coalescing into line segments, potentially training, over parts of southern mo late in the afternoon and evening. Again the main storm concerns will be during the initialization phase with hail and winds with MLCAPE values of 1000-2000 j/kg over far southern MO with abundant deep layer shear. With the potential for training storms in the evening, some guidance is hitting rainfall amounts hard (particularly the 12z ECMWF, NAM, GFS) in south central MO where recent heavy rain and flooding occurred. Will be looking at a possible flood watch for portions of the area, but still ironing out the details. Unseasonable cold is expected late in the week as a deep upper level trough develops over much of the eastern CONUS. Nighttime temperatures will drop below freezing late in the week. Small scale shortwaves moving se through the region may kick off some light/brief periods of rain/snow at times. A modest warming trend develops late in the extended period. && .AVIATION...(For the 06Z TAFS through 06Z Wednesday Night) Issued at 1124 PM CDT TUE MAR 24 2015 Pilots can expect VFR conditions the remainder of tonight with more active weather returning Wednesday. Surface winds will become light and variable overnight. Showers and thunderstorms will develop Wednesday afternoon bringing areas of MVFR conditions. Flight conditions will continue to deteriorate into Wednesday night as a cold front moves across the region and widespread rainfall continues. Widespread MVFR to areas if IFR conditions can be expected Wednesday evening. && .SGF WATCHES/WARNINGS/ADVISORIES... MO...FLOOD WATCH from Wednesday afternoon through Thursday morning FOR MOZ070-071-081>083-090>092-094>098-102>106. KS...NONE. && $$ UPDATE...Foster SHORT TERM...DSA LONG TERM...DSA AVIATION...Foster

subset: commoncrawl


text:
Neural Networks, IEEE Transactions on
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IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.
This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.
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Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming MethodDec-12 2011
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A Discrete-Time Neural Network for Optimization Problems With Hybrid ConstraintsJul-08 2010
Survey of clustering algorithmsMay-09 2005
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Identification and control of dynamical systems using neural networksAug-06 2002
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Popular Articles (February 2015)Includes the top 50 most frequently downloaded documents for this publication according to the most recent monthly usage statistics.
1. A Discrete-Time Neural Network for Optimization Problems With Hybrid Constraints
Cited by: Papers (2)| | Quick Abstract | PDF (2402 KB) | | HTML
Recurrent neural networks have become a prominent tool for optimizations including linear or nonlinear variational inequalities and programming, due to its regular mathematical properties and well-defined parallel structure. This brief presents a general discrete-time recurrent network for linear variational inequalities and related optimization problems with hybrid constraints. In contrary to the existing discrete-time networks, this general model can operate not only on bound constraints, but also on hybrid constraints comprised of inequality, equality and bound constraints. The model has dynamical properties of global convergence, asymptotical and exponential convergences under some weaker conditions. Numerical examples demonstrate its efficacy and performance. View full abstract»
2. Survey of clustering algorithms
Cited by: Papers (683) | Patents (3)| | Quick Abstract | PDF (1528 KB) | | HTML
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed. View full abstract»
3. A comparison of methods for multiclass support vector machines
Cited by: Papers (1289)| | Quick Abstract | PDF (406 KB) | | HTML
Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors View full abstract»
4. Face recognition: a convolutional neural-network approach
Cited by: Papers (213) | Patents (10)| | Quick Abstract | PDF (492 KB)
We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer View full abstract»
5. Identification and control of dynamical systems using neural networks
Cited by: Papers (2214) | Patents (40)| | Quick Abstract | PDF (1792 KB)
It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described View full abstract»
6. An overview of statistical learning theory
Cited by: Papers (781) | Patents (10)| | Quick Abstract | PDF (280 KB)
Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems View full abstract»
7. An introduction to kernel-based learning algorithms
Cited by: Papers (866) | Patents (21)| | Quick Abstract | PDF (516 KB)
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis View full abstract»
8. Face recognition by independent component analysis
Cited by: Papers (478) | Patents (2)| | Quick Abstract | PDF (1405 KB)
A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance. View full abstract»
9. Training feedforward networks with the Marquardt algorithm
Cited by: Papers (722) | Patents (1)| | Quick Abstract | PDF (388 KB)
The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks. The algorithm is tested on several function approximation problems, and is compared with a conjugate gradient algorithm and a variable learning rate algorithm. It is found that the Marquardt algorithm is much more efficient than either of the other techniques when the network contains no more than a few hundred weights View full abstract»
10. Output Feedback Control of a Quadrotor UAV Using Neural Networks
Cited by: Papers (47)| | Quick Abstract | PDF (763 KB) | | HTML
In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture. View full abstract»
11. A general regression neural network
Cited by: Papers (393) | Patents (37)| | Quick Abstract | PDF (804 KB)
A memory-based network that provides estimates of continuous variables and converges to the underlying (linear or nonlinear) regression surface is described. The general regression neural network (GRNN) is a one-pass learning algorithm with a highly parallel structure. It is shown that, even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. The algorithmic form can be used for any regression problem in which an assumption of linearity is not justified View full abstract»
12. Fast and robust fixed-point algorithms for independent component analysis
Cited by: Papers (1375) | Patents (20)| | Quick Abstract | PDF (136 KB)
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. We use a combination of two different approaches for linear ICA: Comon's information theoretic approach and the projection pursuit approach. Using maximum entropy approximations of differential entropy, we introduce a family of new contrast functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model, and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions View full abstract»
13. Support vector machines for histogram-based image classification
Cited by: Papers (293) | Patents (7)| | Quick Abstract | PDF (676 KB)
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y)=e-ρΣi|xia-yia|b with a ⩽1 and b⩽2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input xi→xia improves the performance of linear SVM to such an extend that it makes them, for this problem, a valid alternative to RBF kernels View full abstract»
14. Clustering of the self-organizing map
Cited by: Papers (409) | Patents (2)| | Quick Abstract | PDF (356 KB)
The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time View full abstract»
15. Wavelet networks
Cited by: Papers (478)| | Quick Abstract | PDF (800 KB)
A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions. The basic idea is to replace the neurons by `wavelons', i.e., computing units obtained by cascading an affine transform and a multidimensional wavelet. Then these affine transforms and the synaptic weights must be identified from possibly noise corrupted input/output data. An algorithm of backpropagation type is proposed for wavelet network training, and experimental results are reported View full abstract»
16. A New Formulation for Feedforward Neural Networks
Cited by: Papers (8)| | Quick Abstract | PDF (1079 KB) | | HTML
Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry. Based on this geometrical interpretation, a new set of variables describing neural networks is proposed as a more effective and geometrically interpretable alternative to the traditional set of network weights and biases. Then, this paper develops a new formulation for neural networks with respect to the newly defined variables; this reformulated neural network (ReNN) is equivalent to the common feedforward neural network but has a less complex error response surface. To demonstrate the learning ability of ReNN, in this paper, two training methods involving a derivative-based (a variation of backpropagation) and a derivative-free optimization algorithms are employed. Moreover, a new measure of regularization on the basis of the developed geometrical interpretation is proposed to evaluate and improve the generalization ability of neural networks. The value of the proposed geometrical interpretation, the ReNN approach, and the new regularization measure are demonstrated across multiple test problems. Results show that ReNN can be trained more effectively and efficiently compared to the common neural networks and the proposed regularization measure is an effective indicator of how a network would perform in terms of generalization. View full abstract»
17. OP-ELM: Optimally Pruned Extreme Learning Machine
Cited by: Papers (56)| | Quick Abstract | PDF (259 KB) | | HTML
In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square error) are compared to the original ELM and to three other widely used methodologies: multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate, the proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM. Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online. View full abstract»
18. Universal approximation using incremental constructive feedforward networks with random hidden nodes
Cited by: Papers (218)| | Quick Abstract | PDF (952 KB) | | HTML
According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g:R→R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g:R→R and ∫Rg(x)dx≠0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters. View full abstract»
19. Fuzzy support vector machines
Cited by: Papers (198)| | Quick Abstract | PDF (260 KB) | | HTML
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs) View full abstract»
20. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
Cited by: Papers (98)| | Quick Abstract | PDF (673 KB) | | HTML
In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance View full abstract»
21. Real-Time Simulation of Biologically Realistic Stochastic Neurons in VLSI
Cited by: Papers (13)| | Quick Abstract | PDF (959 KB) | | HTML
Neuronal variability has been thought to play an important role in the brain. As the variability mainly comes from the uncertainty in biophysical mechanisms, stochastic neuron models have been proposed for studying how neurons compute with noise. However, most papers are limited to simulating stochastic neurons in a digital computer. The speed and the efficiency are thus limited especially when a large neuronal network is of concern. This brief explores the feasibility of simulating the stochastic behavior of biological neurons in a very large scale integrated (VLSI) system, which implements a programmable and configurable Hodgkin-Huxley model. By simply injecting noise to the VLSI neuron, various stochastic behaviors observed in biological neurons are reproduced realistically in VLSI. The noise-induced variability is further shown to enhance the signal modulation of a neuron. These results point toward the development of analog VLSI systems for exploring the stochastic behaviors of biological neuronal networks in large scale. View full abstract»
22. Uncertainty Estimation Using Fuzzy Measures for Multiclass Classification
Cited by: Papers (5)| | Quick Abstract | PDF (1741 KB) | | HTML
Uncertainty arises in classification problems when the input pattern is not perfect or measurement error is unavoidable. In many applications, it would be beneficial to obtain an estimate of the uncertainty associated with a new observation and its membership within a particular class. Although statistical classification techniques base decision boundaries according to the probability distributions of the patterns belonging to each class, they are poor at supplying uncertainty information for new observations. Previous research has documented a multiarchitecture, monotonic function neural network model for the representation of uncertainty associated with a new observation for two-class classification. This paper proposes a modification to the monotonic function model to estimate the uncertainty associated with a new observation for multiclass classification. The model, therefore, overcomes a limitation of traditional classifiers that base decisions on sharp classification boundaries. As such, it is believed that this method will have advantages for applications such as biometric recognition in which the estimation of classification uncertainty is an important issue. This approach is based on the transformation of the input pattern vector relative to each classification class. Separate, monotonic, single-output neural networks are then used to represent the "degree-of-similarity" between each input pattern vector and each class. An algorithm for the implementation of this approach is proposed and tested with publicly available face-recognition data sets. The results indicate that the suggested approach provides similar classification performance to conventional principle component analysis (PCA) and linear discriminant analysis (LDA) techniques for multiclass pattern recognition problems as well as providing uncertainty information caused by misclassification View full abstract»
23. Data Mining Methods for Knowledge Discovery
Cited by: Papers (3)| | Quick Abstract | PDF (20 KB)First Page of the ArticleView full abstract»
24. Normalized Mutual Information Feature Selection
Cited by: Papers (89)| | Quick Abstract | PDF (864 KB) | | HTML
A filter method of feature selection based on mutual information, called normalized mutual information feature selection (NMIFS), is presented. NMIFS is an enhancement over Battiti's MIFS, MIFS-U, and mRMR methods. The average normalized mutual information is proposed as a measure of redundancy among features. NMIFS outperformed MIFS, MIFS-U, and mRMR on several artificial and benchmark data sets without requiring a user-defined parameter. In addition, NMIFS is combined with a genetic algorithm to form a hybrid filter/wrapper method called GAMIFS. This includes an initialization procedure and a mutation operator based on NMIFS to speed up the convergence of the genetic algorithm. GAMIFS overcomes the limitations of incremental search algorithms that are unable to find dependencies between groups of features. View full abstract»
25. Orthogonal least squares learning algorithm for radial basis function networks
Cited by: Papers (726) | Patents (7)| | Quick Abstract | PDF (580 KB)
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications View full abstract»
26. Deep Learning Regularized Fisher Mappings
Cited by: Papers (2)| | Quick Abstract | PDF (229 KB) | | HTML
For classification tasks, it is always desirable to extract features that are most effective for preserving class separability. In this brief, we propose a new feature extraction method called regularized deep Fisher mapping (RDFM), which learns an explicit mapping from the sample space to the feature space using a deep neural network to enhance the separability of features according to the Fisher criterion. Compared to kernel methods, the deep neural network is a deep and nonlocal learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable datasets from fewer samples. To eliminate the side effects of overfitting brought about by the large capacity of powerful learners, regularizers are applied in the learning procedure of RDFM. RDFM is evaluated in various types of datasets, and the results reveal that it is necessary to apply unsupervised regularization in the fine-tuning phase of deep learning. Thus, for very flexible models, the optimal Fisher feature extractor may be a balance between discriminative ability and descriptive ability. View full abstract»
27. Domain Adaptation via Transfer Component Analysis
Cited by: Papers (34)| | Quick Abstract | PDF (799 KB) | | HTML
Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification. View full abstract»
28. Input feature selection for classification problems
Cited by: Papers (198)| | Quick Abstract | PDF (411 KB) | | HTML
Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems View full abstract»
29. Support vector machine with adaptive parameters in financial time series forecasting
Cited by: Papers (171)| | Quick Abstract | PDF (558 KB) | | HTML
A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial time series forecasting. The feasibility of applying SVM in financial forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial time series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial forecasting. View full abstract»
30. Simple model of spiking neurons
Cited by: Papers (473) | Patents (7)| | Quick Abstract | PDF (403 KB)
A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC. View full abstract»
31. A Convolutional Neural Network Approach for Objective Video Quality Assessment
Cited by: Papers (27) | Patents (1)| | Quick Abstract | PDF (1071 KB) | | HTML
This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The ambition of this paper is not to present a global perfect quality metric but rather to focus on an original way to use neural networks in such a framework in the context of reduced reference (RR) quality metric. Especially, we point out the interest of such a tool for combining features and pooling them in order to compute quality scores. The proposed approach solves some problems inherent to objective metrics that should predict subjective quality score obtained using the single stimulus continuous quality evaluation (SSCQE) method. This latter has been adopted by video quality expert group (VQEG) in its recently finalized reduced referenced and no reference (RRNR-TV) test plan. The originality of such approach compared to previous attempts to use neural networks for quality assessment, relies on the use of a convolutional neural network (CNN) that allows a continuous time scoring of the video. Objective features are extracted on a frame-by-frame basis on both the reference and the distorted sequences; they are derived from a perceptual-based representation and integrated along the temporal axis using a time-delay neural network (TDNN). Experiments conducted on different MPEG-2 videos, with bit rates ranging 2-6 Mb/s, show the effectiveness of the proposed approach to get a plausible model of temporal pooling from the human vision system (HVS) point of view. More specifically, a linear correlation criteria, between objective and subjective scoring, up to 0.92 has been obtained on - set of typical TV videos View full abstract»
32. Approximation of nonlinear systems with radial basis function neural networks
Cited by: Papers (54)| | Quick Abstract | PDF (416 KB)
A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented. The method uses an n-dimensional raised-cosine type of RBF that is smooth, yet has compact support. The RBF network coefficients are low-order polynomial functions of the input. A simple computational procedure is presented which significantly reduces the network training and evaluation time. Storage space is also reduced by allowing for a nonuniform grid of points about which the RBFs are centered. The network output is shown to be continuous and have a continuous first derivative. When the network is used to approximate a nonlinear dynamic system, the resulting system is bounded-input bounded-output stable. For the special case of a linear system, the RBF network representation is exact on the domain over which it is defined, and it is optimal in terms of the number of distinct storage parameters required. Several examples are presented which illustrate the effectiveness of this technique View full abstract»
33. Data-Based Fault-Tolerant Control of High-Speed Trains With Traction/Braking Notch Nonlinearities and Actuator Failures
Cited by: Papers (4)| | Quick Abstract | PDF (749 KB) | | HTML
This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only- the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations. View full abstract»
34. On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization
Cited by: Papers (62) | Patents (3)| | Quick Abstract | PDF (263 KB) | | HTML
Nonnegative matrix factorization (NMF) is useful to find basis information of nonnegative data. Currently, multiplicative updates are a simple and popular way to find the factorization. However, for the common NMF approach of minimizing the Euclidean distance between approximate and true values, no proof has shown that multiplicative updates converge to a stationary point of the NMF optimization problem. Stationarity is important as it is a necessary condition of a local minimum. This paper discusses the difficulty of proving the convergence. We propose slight modifications of existing updates and prove their convergence. Techniques invented in this paper may be applied to prove the convergence for other bound-constrained optimization problems. View full abstract»
35. Color clustering and learning for image segmentation based on neural networks
Cited by: Papers (46)| | Quick Abstract | PDF (1888 KB) | | HTML
An image segmentation system is proposed for the segmentation of color image based on neural networks. In order to measure the color difference properly, image colors are represented in a modified L*u*v* color space. The segmentation system comprises unsupervised segmentation and supervised segmentation. The unsupervised segmentation is achieved by a two-level approach, i.e., color reduction and color clustering. In color reduction, image colors are projected into a small set of prototypes using self-organizing map (SOM) learning. In color clustering, simulated annealing (SA) seeks the optimal clusters from SOM prototypes. This two-level approach takes the advantages of SOM and SA, which can achieve the near-optimal segmentation with a low computational cost. The supervised segmentation involves color learning and pixel classification. In color learning, color prototype is defined to represent a spherical region in color space. A procedure of hierarchical prototype learning (HPL) is used to generate the different sizes of color prototypes from the sample of object colors. These color prototypes provide a good estimate for object colors. The image pixels are classified by the matching of color prototypes. The experimental results show that the system has the desired ability for the segmentation of color image in a variety of vision tasks. View full abstract»
36. Neighborhood based Levenberg-Marquardt algorithm for neural network training
Cited by: Papers (51)| | Quick Abstract | PDF (196 KB) | | HTML
Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the behavior of a recently proposed variation of this algorithm is studied. This new method is based on the application of the concept of neural neighborhoods to the LM algorithm. It is shown that, by performing an LM step on a single neighborhood at each training iteration, not only significant savings in memory occupation and computing effort are obtained, but also, the overall performance of the LM method can be increased. View full abstract»
37. Improved Computation for Levenberg–Marquardt Training
Cited by: Papers (50)| | Quick Abstract | PDF (645 KB) | | HTML
The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm. Quasi-Hessian matrix and gradient vector are computed directly, without Jacobian matrix multiplication and storage. The memory limitation problem for LM training is solved. Considering the symmetry of quasi-Hessian matrix, only elements in its upper/lower triangular array need to be calculated. Therefore, training speed is improved significantly, not only because of the smaller array stored in memory, but also the reduced operations in quasi-Hessian matrix calculation. The improved memory and time efficiencies are especially true for large sized patterns training. View full abstract»
38. Face recognition using LDA-based algorithms
Cited by: Papers (185) | Patents (4)| | Quick Abstract | PDF (471 KB) | | HTML
Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods. View full abstract»
39. Tuning of the structure and parameters of a neural network using an improved genetic algorithm
Cited by: Papers (185)| | Quick Abstract | PDF (566 KB)
This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It is also shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network. View full abstract»
40. Learning Speaker-Specific Characteristics With a Deep Neural Architecture
Cited by: Papers (4)| | Quick Abstract | PDF (672 KB) | | HTML
Speech signals convey various yet mixed information ranging from linguistic to speaker-specific information. However, most of acoustic representations characterize all different kinds of information as whole, which could hinder either a speech or a speaker recognition (SR) system from producing a better performance. In this paper, we propose a novel deep neural architecture (DNA) especially for learning speaker-specific characteristics from mel-frequency cepstral coefficients, an acoustic representation commonly used in both speech recognition and SR, which results in a speaker-specific overcomplete representation. In order to learn intrinsic speaker-specific characteristics, we come up with an objective function consisting of contrastive losses in terms of speaker similarity/dissimilarity and data reconstruction losses used as regularization to normalize the interference of non-speaker-related information. Moreover, we employ a hybrid learning strategy for learning parameters of the deep neural networks: i.e., local yet greedy layerwise unsupervised pretraining for initialization and global supervised learning for the ultimate discriminative goal. With four Linguistic Data Consortium (LDC) benchmarks and two non-English corpora, we demonstrate that our overcomplete representation is robust in characterizing various speakers, no matter whether their utterances have been used in training our DNA, and highly insensitive to text and languages spoken. Extensive comparative studies suggest that our approach yields favorite results in speaker verification and segmentation. Finally, we discuss several issues concerning our proposed approach. View full abstract»
41. Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities
Cited by: Papers (53)| | Quick Abstract | PDF (1054 KB) | | HTML
In this paper, robust adaptive neural network (NN) control is investigated for a general class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems with unknown control coefficient matrices and input nonlinearities. For nonsymmetric input nonlinearities of saturation and deadzone, variable structure control (VSC) in combination with backstepping and Lyapunov synthesis is proposed for adaptive NN control design with guaranteed stability. In the proposed adaptive NN control, the usual assumption on nonsingularity of NN approximation for unknown control coefficient matrices and boundary assumption between NN approximation error and control input have been eliminated. Command filters are presented to implement physical constraints on the virtual control laws, then the tedious analytic computations of time derivatives of virtual control laws are canceled. It is proved that the proposed robust backstepping control is able to guarantee semiglobal uniform ultimate boundedness of all signals in the closed-loop system. Finally, simulation results are presented to illustrate the effectiveness of the proposed adaptive NN control. View full abstract»
42. Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm
Cited by: Papers (146) | Patents (2)| | Quick Abstract | PDF (264 KB)
Presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis (1996) on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data View full abstract»
43. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification
Cited by: Papers (66)| | Quick Abstract | PDF (950 KB)
In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification View full abstract»
44. Deterministic Learning and Rapid Dynamical Pattern Recognition
Cited by: Papers (24)| | Quick Abstract | PDF (1433 KB) | | HTML
Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns. First, it is shown that a time-varying dynamical pattern can be effectively represented in a time-invariant and spatially distributed manner through deterministic learning. Second, a definition for characterizing similarity of dynamical patterns is given based on system dynamics inherently within dynamical patterns. Third, a mechanism for rapid recognition of dynamical patterns is presented, by which a test dynamical pattern is recognized as similar to a training dynamical pattern if state synchronization is achieved according to a kind of internal and dynamical matching on system dynamics. The synchronization errors can be taken as the measure of similarity between the test and training patterns. The significance of the paper is that a completely dynamical approach is proposed, in which the problem of dynamical pattern recognition is turned into the stability and convergence of a recognition error system. Simulation studies are included to demonstrate the effectiveness of the proposed approach View full abstract»
45. Artificial neural networks for solving ordinary and partial differential equations
Cited by: Papers (61) | Patents (1)| | Quick Abstract | PDF (424 KB)
We present a method to solve initial and boundary value problems using artificial neural networks. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial/boundary conditions and contains no adjustable parameters. The second part is constructed so as not to affect the initial/boundary conditions. This part involves a feedforward neural network containing adjustable parameters (the weights). Hence by construction the initial/boundary conditions are satisfied and the network is trained to satisfy the differential equation. The applicability of this approach ranges from single ordinary differential equations (ODE), to systems of coupled ODE and also to partial differential equations (PDE). In this article, we illustrate the method by solving a variety of model problems and present comparisons with solutions obtained using the Galerkin finite element method for several cases of partial differential equations. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed View full abstract»
46. An analog CMOS chip set for neural networks with arbitrary topologies
Cited by: Papers (11) | Patents (3)| | Quick Abstract | PDF (344 KB)
An analog CMOS chip set for implementations of artificial neural networks (ANNs) has been fabricated and tested. The chip set consists of two cascadable chips: a neuron chip and a synapse chip. Neurons on the neuron chips can be interconnected at random via synapses on the synapse chips thus implementing an ANN with arbitrary topology. The neuron test chip contains an array of 4 neurons with well defined hyperbolic tangent activation functions which is implemented by using parasitic lateral bipolar transistors. The synapse test chip is a cascadable 4×4 matrix-vector multiplier with variable, 10-b resolution matrix elements. The propagation delay of the test chips was measured to 2.6 μs per layer View full abstract»
47. High-speed face recognition based on discrete cosine transform and RBF neural networks
Cited by: Papers (75)| | Quick Abstract | PDF (1948 KB) | | HTML
In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher's linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating and invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness. View full abstract»
48. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks
Cited by: Papers (104) | Patents (1)| | Quick Abstract | PDF (396 KB)
Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience View full abstract»
49. Adaptive neural control of uncertain MIMO nonlinear systems
Cited by: Papers (162)| | Quick Abstract | PDF (546 KB) | | HTML
In this paper, adaptive neural control schemes are proposed for two classes of uncertain multi-input/multi-output (MIMO) nonlinear systems in block-triangular forms. The MIMO systems consist of interconnected subsystems, with couplings in the forms of unknown nonlinearities and/or parametric uncertainties in the input matrices, as well as in the system interconnections without any bounding restrictions. Using the block-triangular structure properties, the stability analyses of the closed-loop MIMO systems are shown in a nested iterative manner for all the states. By exploiting the special properties of the affine terms of the two classes of MIMO systems, the developed neural control schemes avoid the controller singularity problem completely without using projection algorithms. Semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop of MIMO nonlinear systems is achieved. The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. The proposed schemes offer systematic design procedures for the control of the two classes of uncertain MIMO nonlinear systems. Simulation results are presented to show the effectiveness of the approach. View full abstract»
50. Input space versus feature space in kernel-based methods
Cited by: Papers (359) | Patents (18)| | Quick Abstract | PDF (1216 KB)
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data View full abstract»
Aims & Scope
IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware.
This Transactions ceased production in 2011. The current retitled publication is IEEE Transactions on Neural Networks and Learning Systems.
Further Links
Aims & Scope
IEEE Transactions on Neural Networks is devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.
Persistent Link: http://ieeexplore.ieee.org/servlet/opac?punumber=72 More »
Frequency: 12
Publication Details:IEEE Transactions on Neural Networks
- Bioengineering
- Communication, Networking & Broadcasting
- Components, Circuits, Devices & Systems
- Computing & Processing (Hardware/Software)
- Engineered Materials, Dielectrics & Plasmas
- Engineering Profession
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- Geoscience
- Nuclear Engineering
- Photonics & Electro-Optics
- Power, Energy, & Industry Applications
- Signal Processing & Analysis
Derong Liu
Key Laboratory of Complex Systems and Intelligence Science
Institute of Automation
Chinese Academy of Sciences
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- ( 1990 - 2011 ) Neural Networks, IEEE Transactions on


text:
So I took my newly acquired Sig SP2022 out for the first time yesterday. It was the first time I've been to an outdoor range, which was a nice change from the indoor ranges I've been to.
Put 200 rounds through it (100 Winchester white box and 100 Federal from Walmart) without a hiccup. The single action was really smooth but the double action seemed like a really long pull. I had trouble keeping steady throughout the entire pull. It seemed accurate enough once I got used to the sights. The grip felt nice but there was a little more recoil than I anticipated. Once I got used to it, it seemed real smooth.
The target was at 7 yards. I went shooting with my father, the top and bottom right targets were mine. 25 rounds at each. I thought it was decent considering how much of a beginner I am.. but we're both not very good.
I can't wait to put some more rounds through it.
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Wow nice gun! I really like Sigs a lot. The 226 will most likely be my next gun.
Nice. And I know exactly where you are shooting lol.
Just an update, I didn't take it out for a few months but I've taken it out 2 of the last 4 weekends. I've put 700 (federal) rounds through it so far with no hiccups at all. Overall, I'm very pleased with its performance and it's a joy to shoot.
I actually sold my 228 for a Sig Pro. Trigger is nice and I like the adjustable grip panels. Nice guns.


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City of Medford Issues Call For Youth Baseball UmpiresBy Rich Rosenthal
The Medford Parks and Recreation Department is issuing a call for qualified umpires to work youth baseball tournaments at U.S. Cellular Community Park.
Medford Announces ASA Men's Slow-Pitch Softball Qualifier SeriesBy Rich Rosenthal
The Medford Parks and Recreation Department is offering a qualification incentive for men's slow-pitch softball teams intending to compete in the Amateur Softball Association national tournament at U.S. Cellular Community Park, Sept. 4-7, 2015.
USCCP Sets Economic Impact RecordBy Rich Rosenthal
U.S. Cellular Community Park set an all-time record for economic impact in 2014.
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Why Chuck Clayton the cartoonist? by Wug
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Archie Hot Wheels by DeCarlo Rules
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Celebrities who should visit Riverdale. by Wug
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The Burger King in Janesville, Wisconsin on the wonderful Milton Avenue.
RetailByRyan95, hurricandylan, and 4 other people added this photo to their favorites.
The old Burger King logo wasn't that bad at all!
I'm surprised that Burger King still has it's old logo. All of the Burger Kings around here (Erie, Pa.) have the current logo.
This was taken in 2003. It could have been replaced, I haven't really paid attention.
It's rare to see the 1969-1/2 BK logo with all the black trim areas of the sign painted blue.


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Stage 2 - vimeo.com/60445284
3ds max, thinking particles (no bullet, old good SC), a lot of caching, RnD in collaboration with JBond and some prefracturing using his great script Advanced Fragmenter (vimeo.com/56238735)
10 days of pain (actually 8 for rnd, sim, oh f*ck no!, resim, cache and render and 2 for struggling with bugs and damaged scene files)
Will add structure with stairs to the far left side (Stage 2) and a lot of dust (Stage 3). Somewhere inbetween plan to add materials (have all these pillars unwraped to couple of 16k maps - don't know why=)
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Thunder notebook: Scott Brooks preaching to Kendrick Perkins about technical fouls
Thunder reserve center Cole Aldrich rejoined the team Wednesday after missing Tuesday's contest at Memphis to visit his ailing grandfather in Minnesota.
"He's not doing very well," Aldrich said of his father's 83-year-old father. "I was satisfied with going home and seeing him, spending as much time as I could with him. It's one of those hard things you go through and basketball helps get it off your mind."
The Thunder has Thursday off and will practice at 11 a.m. Friday in preparation for their lone game this season against New York at 7 p.m. on Saturday inside Chesapeake Energy Arena. That is followed by a three-game road trip that is spread over six days — Boston (Monday), Washington (Wednesday) and New Jersey (Jan. 21).
The Thunder has played 12 games in the first 18 days of the season, but OKC's next back-to-back won't be until Feb. 6-7. … With Philadelphia's loss at New York and the Los Angeles Lakers winning in overtime at Utah on Wednesday night, the Thunder has the league's longest active winning streak at five. … Kevin Durant's streak of 24 made free throws ended with a first-quarter miss. A career 87.9-percent free-throw shooter entering the game, Durant is shooting just 79.8 percent (71 for 89) this season. … OKC entered the game leading the league in free-throw shooting at 81.0 percent, but shot just 69.2 percent (18 for 26) from the line Wednesday. … New Orleans (3-7) is now 0-7 when the opposing team scores 90-plus points.
"They have three All-Stars. I think (James) Harden is an All-Star right now." — Williams on OKC


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3 female and 2 males. Very socialized parents on site and they are very docile. Requires room to run and lots of attention. Ready to go home first week of may. Taking deposits of $200 paypal or cashiers checks. Up to date on shots tails docked and dew claws removed. ...
Mar 23 -
Cincinnati :
Cincinnati - Dogs & puppies
Blue and silver weimaraner puppies. 5 weeks old. Ready for homes March 22-27 2015. See www. Facebook.com/nrvweims to learn more. Call us anytime.
Mar 11 -
Blacksburg :
Shawsville - Dogs & puppies
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I was interviewed by the gang at GeekDad on their Great Big Beautiful podcast and I'm really happy with how this one turned out. we talk about working with the Disney Imagineering team on Figment, finding the voice for a series, and juggling multiple projects. Give it a listen!
Mega-Zub Comic Release Day!
Thanks to the shipping strike that happened in California and a couple printing delays a bunch of my comic titles have synced up, leading to the craziest new comic book day of my career so far:
6 new books from 4 different publishers all arriving in comic shops TODAY!
Yeah, it's ridiculous.
Click on any of the covers below to read preview pages from each of the books:
As always, thank you for your support!
Zub at Emerald City Comicon This Weekend!
This weekend convention season 2015 kicks into overdrive with the wondrous Emerald City Comicon in Seattle, Washington! It's one of my favorite shows every year and this time it's going to be extra-special as I'll be there celebrating the release of WAYWARD Vol. 1 and the arrival of WAYWARD #6 and SKULLKICKERS #31!
Here is where you can find some of my artistic collaborators and I at the show:
JIM ZUB – Table HH-11
ANDY SURIANO (Samurai Jack artist) – Table HH-12
EDWIN HUANG (Skullkickers artist) – Booth 1702
MAX DUNBAR (D&D: Legends of Baldur's Gate artist) – Table HH-10
ZACK DAVISSON (Wayward essays) – Table F-14
Image and Emerald City Comicon teamed up to create a limited hardcover edition of Wayward Vol. 1: String Theory for the show. It's limited to only 250 copies and I'll be selling them at the table for $30 each as long as they last.
I'll also have a selection of issues, sketch covers, trade paperbacks, and variants from my other series there as well: Skullkickers, Samurai Jack, D&D: Legends of Baldur's Gate, Conan-Red Sonja, Munchkin, Wayward, and more!
Here's a list of signings and panels I'll be on over the weekend:
FRIDAY MARCH 27th
Writers Unite: Pitching and Writing Comic Stories
11:30AM – 12:20PM in Hall F (TCC 304)
Comics are everywhere and publishers are looking for fresh ideas from a new generation of talent. Listen closely as creators Charles Soule (Death of Wolverine, Letter 44), Kieron Gillen (Young Avengers, Wicked + the Divine), and moderator Jim Zub (Samurai Jack, Wayward) discuss pitching story concepts and offer advice on climbing to the top of the treacherous submission mountain, along with writing techniques and amusing anecdotes.
Wayward and Weird Japan
2:50PM – 3:40PM in Hall E (TCC 303)
Dive into the weird world of the hit series Wayward from Image Comics. Series writer Jim Zub and back matter writer and Japanese folklore expert Zack Davisson take you on a tour of the mystical menagerie of Japan's Yōkai.
4:00PM – 4:50PM Gail Simone and Jim Zub sign at the Dark Horse booth (BOOTH 802)
SATURDAY MARCH 28th
Convention Horror Stories
1:50PM – 2:40PM in Hall E (TCC 303)
Join Katie Cook (My Little Pony: Friendship Is Magic) and Jim Zub (Samurai Jack) for their fan-favorite con horror stories panel! What's it like working as a pro in the business on the convention 'circuit'? Ridiculous, embarrassing, always entertaining. Some stories will make you laugh out loud, some will make you cringe! This panel is recommended for those 18+
D&D: Legends of Baldur's Gate #5 Reviews
After a shipping delay, Legends of Baldur's Gate #5, wrapping up our story, has finally arrived. Let's see what reviewers thought of it…
• Bleeding Cool: "I can only hope that this isn't the last time we see this fun team together."
• Fanboy Comics: "The story sings, and it's a fight that we've been waiting for from the first moment we heard the word Dragon, and it doesn't disappoint."
• Fanboy Nation: "Legends of Baldur's Gate has been a blast. Here's hoping that issue #5 is not the end!"
• Geeks With Wives: 8.5/10 "Once again, Zub nails the script and dialogue. The conversations and narration were wonderfully done, and captured the spirit of the source material."
• Reading With a Flight Ring: "the interiors are simply stunning to behold. If you need proof pick this up and flip through it the visage of the Dragon on any page is a thing of beauty."
This Weekend: Toronto Comicon!
This weekend, March 20-22, I'll be a guest of the Toronto Comicon, set up for the show at ARTIST ALLEY TABLE P-22. It's my first comic convention of the year and I'm excited to be kicking the 2015 season off with a hometown show.
I have a limited number of advance copies of WAYWARD Vol. 1: String Theory, WAYWARD #6, and SKULLKICKERS #31, so make sure you swing by to grab a copy before the regular release date. I'll also have trades, comics, and sketch covers for a bunch of my other series, including Samurai Jack, Dungeons & Dragons: Legends of Baldur's Gate, Conan-Red Sonja, and more!
If you're in the Greater Toronto Area and can make it out to the show I hope to see you there!
(PS: I might be late on Friday as I have to head right from my teaching day job down to the convention centre, so between crazy Toronto traffic and set-up I might be delayed.)
Wayward #9 Solicitation
Arriving in June… Preorder now!
story: JIM ZUB
art / cover A: STEVE CUMMINGS & TAMRA BONVILLAIN
cover B: GURIHIRU
Tokyo is a battleground and blood will be spilled.
COVER A is part four of a five-part panorama by STEVE CUMMINGS & TAMRA BONVILLAIN. Buy all five issues of this story arc to fit them together into a massive WAYWARD illustration.
COVER B is a variant by acclaimed Japanese illustrators GURIHIRU.
Arriving in June: Skullkickers #100?!
In June, Skullkickers comes to an end with the wrap-up of our sixth and final story arc "Infinite icons of the Endless Epic". We didn't want this momentous occasion to go unnoticed, so we decided to do what the other comic publishers do when they want to celebrate a big story event – we bull$#@&ted a big number on the cover because we could.
Here's the June ordering solicitation:
story: JIM ZUB
art: EDWIN HUANG & MISTY COATS
cover: CHRIS STEVENS & ESPEN GRUNDETJERN
"INFINITE ICONS OF THE ENDLESS EPIC" Finale
ISSUE 100?! Hey, if other publishers can just slap any old number on a cover and call it an anniversary, then we want in on that action for our final issue.
Yup, this is the big finish. All skulls must be kicked.
Join us in sending off the series in style with a big 3-digit number and tweet with the hashtag #SK100 to let us know what happened in the 66 issues in between. The best entries get a place in Skullkickers history.
That's right, we're jumping straight to #100 to finish the series and are letting our fans fill in the 66 issue gap with the Twitter hashtag #SK100. Tell us in 140 characters a summary of what happened in an unpublished issue of Skullkickers (from #34-99) and the best ones will be listed in the back of our final issue. Anyone could be one tweet from their first Twitter comic writing credit.
So few creator-owned comics ever have an issue 100. We're feeling quite honored to share this rarified air with Spawn, Savage Dragon, Walking Dead, and Invincible. It's a milestone for creator-owned comics and we couldn't do it without Image Comics and our dedicated fanbase.
Thank you for
34 100 issues of love and support.
Geek Dad Interview: Steve Cummings and Jim Zub
Over on the Geek Dad website Steve Cummings and I talk about working together on Wayward and some of our plans for the upcoming second story arc.
Geeked Out Nation Advance Review for WAYWARD Vol. 1: 10/10!
Geeked Out Nation has the first advance review of WAYWARD Vol. 1: String Theory and they give it their 'Trade of the Month' with a perfect score.
"Wayward is a fantastic series and this trade is absolutely something you need to pick up. It's for fans of manga, magic, Japanese culture and kick ass female leads. It features beautiful art and thoughtful back matter. Wayward is a winner and easily deserves the title of 'Trade of the Month'."
Click on through to read an interview with me and an extensive review of our first story arc.
Wayward Analysis on Panels
Over on Panels.net, Jeanette Solomon talks Wayward and protagonist Rori Lane's difficulties with self harm. Give it a read.


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Bill McCloskey | July 2, 2009 | Comments
E-mail marketers will need to change their approach to ensure inbox deliverability in the next five years. Here are some ways to begin.
The growth of the e-mail channel -- due to a weakened economy and e-mail's proven high ROI (define) -- makes it more important than ever for companies to invest in their e-mail marketing programs. Within the next five years, e-mail marketers will have to change their strategy and tactics to improve and ensure inbox deliverability.
Forrester Research predicts spending on e-mail marketing will increase to $2 billion by 2014, up from an estimated $1.2 billion this year. The consultancy also predicts consumers will receive more than 9,000 e-mail marketing messages a year by 2014. The increase in marketing spend will be fueled by falling CPMs (define), high ROI, and the growing use of social e-mail accounts.
Content and deliverability are two key elements to any e-mail marketing program. To improve deliverability, marketers must focus on the following: retention e-mails, IP address reputation, segmentation, dynamic content, and social sharing.
Retention e-mails are permission-based e-mail messages sent with the goal of keeping customers. They provide valuable, beneficial, and entertaining information to the reader. Forrester estimates retention e-mails in five years will account for more than one-third of all marketing messages in consumer's inboxes.
The next question: How do you ensure that your message gets to your customers' inboxes? E-mail deliverability is determined by IP reputation. If your e-mail content is pristine and perfect, but send it from an IP address with a bad reputation chances are your messages will never make it to a person's inbox. ISPs will block e-mail based on IP reputation. Even with a dedicated IP and pristine reputation, there is information to suggest that close proximity to an IP address with a poor rep can tarnish those IP addresses around it.
Once you make it into the inbox, you must ensure your messages are relevant. You want your e-mail to be the one the reader is anticipating. Smart marketers use techniques to create the sense of an "insiders club" by providing offers and information only available via the inbox. Making offers exclusive to the e-mail channel can help build anticipation and improve open rates. Using segmentation and dynamic content also increase the value of messages. Remember, less is more. Only provide information relevant to your segmented audience. Too much information will overwhelm readers, causing them to lose interest in your messages.
Increase your e-mail marketing effectiveness by engaging your customer through social sharing. Many new programs link e-mail marketing campaigns with social media campaigns. Sharing information found in newsletters and e-mails by uploading the article to customers' Facebook accounts has started to become more popular than the forward-to-a-friend option. In addition to sharing information with your Facebook, LinkedIn, and Twitter accounts, new programs are bridging the gap between e-mail and social media. StrongMail is set to release a tool in September that will provide marketers the opportunity to send messages directly into the e-mail boxes of Facebook fans and track the interaction.
It's important for your organization's upper management to realize that the surest way to increased sales and happy customers is a well-run, well-monitored e-mail program. Other programs may get cut, but an investment in e-mail will pay for itself many times over.
Want to learn more?
Attend ClickZ Live New York March 30 - April 1. With over 15 years' experience delivering industry-leading events, ClickZ Live brings together over 60 expert speakers to offer an action-packed, educationally-focused agenda covering all aspects of digital marketing. Register today!
Bill McCloskey is the founder and chief evangelist for Email Data Source, a competitive intelligence resource for e-mail marketers. He was named one of online advertising's 50 most influential people by "Media" magazine and one of the 100 people to know by "BtoB Magazine." He's been a recognized pioneer in interactive advertising for over 10 years.
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Venison...oddly enough, given that we are surrounded by countryside, we've never had a reliable source of venison, until now. Just after Christmas we got our first carcass from over the border in Dorset, all of about 15 miles away. We've been experimenting with cutting and cooking it and the results have made a real impact on the menu - "the best venison I've ever had" has been the response several times. Needless to say we are chuffed to bits and venison will now be a staple on our menu.
Doing our bit towards the Mutton Renaissance...the Somerset Mutton we've been cooking has also attracted a number of devotees and the latest incarnation of the Duo of Mutton really shows it at its best.
We have a RESTAURANT TripAdvisor page too! Click here!
And we are now getting reviews on OpenTable too! Click here!
Sample Menus: Dinner Friday & Saturday Lunch Sunday Lunch
A sideways glance at the chef. Or is that a profile?
Jools claims to have had the longest and most expensive apprentiship in cheffery. 25 years of lunching (and often dining) in his previous life running an advertising studio in Soho. (The third oldest profession.) "Learning what it should taste like is the difficult bit - the rest is just technique." Direction from old friends made along the way followed by 18 years at The Pilgrims Restaurant defining and refining his repertoire has produced a distinctive style and a unique menu.
Opening Hours
We have decided that during the week we need to allow ourselves more time to concentrate on both our evening service and our accommodation. So from the beginning of November The Pilgrims will open for Dinner from Tuesday to Saturday as usual but only open for Lunch on Fridays and Saturdays, and the last Sunday of each month of course.
Reservations Only
And finally, again to help make Dinner at The Pilgrims as enjoyable as it can be, we are changing to 'reservations only please' in the evenings. Friday and Saturday lunch can be a bit hectic so booking is advisable then too if you can.
The Ultimate Sunday Lunch
On the ultimate Sunday* of every month The Pilgrims' Restaurant hosts The Ultimate Sunday Lunch. A sirloin of beef is roasted to rare perfection, accompanied by roast potatoes, yorkshire pudding, fresh horseradish sauce, seasonal vegetables and, of course, a proper gravy. We think, in all the modesty we can raise, (admittedly not much in this instance) that it is an experience not to be missed. A la Carte dishes are also available.
Dine Finely
If we say so ourselves Jools' skill is in feeding a small number of people with simply wonderful food, in an unhurried manner, and that's the way we try to make the restaurant run. Our dining guests come here to dine finely - as opposed to being attracted by promises of Faine Daining. A concept which is laudable and appropriate in the hands of a handful of chefs touched with genius, such as Raymond Blanc, but which has been abused and debased by an army of lesser cooks as an excuse for ripping off their customers
With the produce we have available to us the most important quality in a chef is restraint. Let the intrinsic quality work its magic then leave it alone. Bring out the flavours but keep it simple. Our presentation may seem a little stark, not as primped as devotees of Masterchef may expect, but Jools just can't bring himself to 'gratuitously garnish' even if it fashionable.
The best from the 'Appellation of Somerset' on a plate
We find the word 'local' crops up a lot when The Pilgrims is discussed, not necessarily in the traditional pub sense, although many of our regular diners are 'locals' who see us as their neighbourhood restaurant. It's more that we have become a bit of a focal point for good things produced hereabouts, a showcase for and champion of many of the wonderful artisan crafted drinks and foodstuffs from what would be the Appellation of Somerset, if there were any justice. We're not fanatical about it, but we are more than a bit passionate. If our food is 'Inspired' then this is what inspires it.
This means that our menu is something of a moveable feast - changing at least monthly and often daily - with the seasonal availability of fresh ingredients. Almost all of our vegetables are locally and organically grown. Our cheeses are all made within 30 miles, they just happen to be world-class cheeses; milk and cream come from Olive Farm's Guernsey herd, a mile away for any self- respecting crow; our bacon is from Denhay's in Bridport; eels are smoked at Brown & Forrest on the Somerset levels; fresh fish comes direct from Bridport; Jon Thorner sources all our meat from local farms; salads are grown by Charles Dowding and most of our vegetables by a local organic co-operative. Jools makes our bread from locally milled organic spelt and wheat flours. Lovington's Ice Creams and Cottage Brewing are in Lovington.
Slow Food? Well, it's certainly not fast food!
With the exception of the ice cream everything we serve at The Pilgrims is home-made. Virtually nothing comes ready made out of a packet. Everything is prepared and cooked by Jools with help from Sally. Whatever you choose will be cooked to order which means that you may have to wait a little longer than we are all used to in this frenetic age. But the table is yours for the evening and although we work as quickly as we can, we assume that you would rather have something worth waiting for than "fast food".
Families are always welcome to dine at The Pilgrims but, in our own experience, children don't like children's menus. So we don't have one. And some people just have smaller appetites. Most of our main courses can be prepared in a smaller portion, for a smaller price.
Wow, what a Year!! In 2014 we were on Taste of the West's shortlist of 5 for Best Restaurant in the whole South West - and won 'Best B&B in the South West' The AA gave us a 2nd ROSETTE! And we got TWO Gold Awards from Taste of the West! One for the Restaurant and One for the Rooms.
As you can imagine this level of commitment has led to the odd award or three. Over the years we have won Gold from Taste of the West FOUR times, (and Silver once, obviously an off day), we've been Somerset Life's Dining Pub of the Year, and Jools won their Chef of the Year. We now appear in most of the serious guides and were ridiculously proud to be one of only fifty establishments in the UK to feature in Alastair Sawday's book 'Eat Slow'. Nowadays we don't often enter ourselves for any awards – they are too much of a distraction. We are rigorously assessed every year by VisitBritain and the AA - achieving Four Stars, a Gold Award, TWO AA Rosettes and a Breakfast Award – which tells anybody what they need to know about us. From a more direct, subjective perspective a lot of guests have been very kind to us on TripAdvisor and our online booking site, Eviivo.


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The Change…..
The snow crunched under my feet as I took the trail down towards the Lodge. I do love the sound. The way the mountains are silent when the snow falls. The stillness that seems to permeate the entire world. We … Continue reading
It's almost time….
The days are beginning to lengthen now. It has been a perfect January 2015 and surprisingly un-winter like. After a month under the clouds, we have had a fabulous four days of warmer weather and bright, bright blue sky. It is absolutely amazing … Continue reading
The New Year!
The walk down the old road was one we've taken hundreds of times before. The dogs bounded ahead, smelling the crispness in the air and celebrating life like they always do. The creek flowed wild and free beside the road, … Continue reading


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Bought 4 of these for extra seating at a holiday party. They are AWESOME. I have used them as ottomans, extra seating, and a makeshift table to hold a glass of wine or cheeseplate. They are simple and perfect!!
I was replacing the last footstool I bought from you. It was really cute, a round patchwork one, but after a few months it started separating at all the seams. Very poor quality. After all, it's made to put your feet on. So, silly me, I bought a second one. This time the round one. As I said it is too light weight and higher than I wanted, but I'll use it until it falls apart, which shouldn't be too long. Maybe it's my fault for buying faux leather and expecting it to be good quality. Won't be buying another one from your company. Sorry.
The footstool is as described. It was a little lighter in weight than I expected it to be, but it had a richer appearance than expected. It is ideal as an extra seat.
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In what will hopefully not inspire even more tacky billboards and even tackier videos, always the bridesmaid never the bride talent agency William Morris Endeavor is buying entertainment and sports marketing giant IMG Worldwide. » 12/18/13 5:17pm 12/18/13 5:17pm
Russell Wilson may have tossed up a Hail Mary to become Jesus' favorite quarterback, but Tim Tebow just inked a deal with the devil in an effort to resume his righteous place at the top of the flock. » 11/11/13 2:12pm 11/11/13 2:12pm