This application is a continuation application of U.S. application Ser. No. 11/324,011, filed Dec. 30, 2005, for which the contents are incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE TECHNOLOGY 
     The present technology relates to increasing learning speed when a large number of binary classifiers need to be trained together. 
     BACKGROUND 
     Large margin or regularized classifiers such as support vector machines (SVMs), Adaboost, and Maximum Entropy ((Maxent) in particular Sequential L1-Regularized Maxent) are obvious choices for use in semantic classification. (For additional information on these classifiers see Vladimir N. Vapnik, Statistical Learning Theory, John Wiley &amp; Sons, 1998; see also Yoav Freund and Robert E. Schapire, Experiments with a New Boosting Algorithm, In Proceedings of ICML &#39;96, pages 148-156, 1996; see also Miroslav Dudik, Steven Phillips, and Robert E. Schapire, Performance Guarantees for Regularized Maximum Entropy Density Estimation, In Proceedings of COLT &#39;04, Banff, Canada, 2004, Springer Verlag; see also Miroslav Dudik, Steven Phillips, and Robert E. Schapire, Performance Guarantees for Regularized Maximum Entropy Density Estimation, In Proceedings of COLT &#39;04, Banff, Canada, 2004, Springer Verlag). 
     These linear classifiers are well known in the art as all three large margin or regularized classifiers offer learning processes that are scalable with runtime implementations that may be very efficient. These algorithms give users three ways to train a linear classifier using very different frameworks. Each of these algorithms may be used across multiple processors or clusters of computers (parallelization) to increase learning speed. 
     SVMs look for a separating hyperplane  1002  with a maximum separation margin  1010  between two classes, as shown in  FIG. 1 . A first set of classes  1006  and a second set of classes  1008  are separated on the separation hyperplane. The hyperplane  1002  can be expressed as a weight vector. The margin is the minimum distance between the projections of the points of each class on the direction of the weight vector. The maximum margin  1010  is shown in  FIG. 1  as the distance between the first set of classes  1006  and the second set of classes  1008 .  FIG. 1  represents hard margin SVMs, where classification errors on the training set are not permitted. For discussion purposes in the remaining description of the invention, SVMs are generalized as soft margin SVMs, which allow some errors, i.e. vectors that are inside or on the wrong side of the margin. 
     AdaBoost incrementally refines a weighted combination of weak classifiers. AdaBoost selects at each iteration a feature k and computes, analytically or using line search, the weight w k  that minimizes a loss function. In the process, the importance of examples that are still erroneous is “adaptively boosted”, as their probability in a distribution over the training examples that is initially uniform is increased. Given a training set associating a target class y i  to each input vector x i , the AdaBoost sequential algorithm looks for the weight vector w that minimizes the exponential loss (which is shown to bound the training error): 
     
       
         
           
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     AdaBoost also allows a log-loss model, where the goal is to maximize the log-likelihood log of the training data log(Π i P(y i |x i )). The posterior probability to observe a positive example is 
     
       
         
           
             
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     Finally, Maxent relies on probabilistic modeling. In particular, ones assumes that the classification problem is solved by looking for the class y which maximizes a distribution argmax P(y|x). (See; Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra, A maximum entropy approach to natural language processing, Computational Linguistics, 22(1):39-71, 1996). 
     First, how well this distribution matches the training data is represented by constraints which state that features must have the same means under the empirical distribution (measured on the training data) and under the expected distribution (obtained after the training process). Second, this distribution must be as simple as possible. This can be represented as a constrained optimization problem: find the distribution over training samples with maximum entropy that satisfies the constraints. Using convex duality, one obtains as a loss function the Maximum Likelihood. The optimization problem is applied to a Gibbs distribution, which is exponential in a linear combination of the features: 
               P   ⁡     (   x   )       =       exp   ⁡     (       w   T     ⁢   x     )       Z           
with Z=Σ i=1   M exp(w T x i ).
 
     Sequential L1-Regularized Maxent is one of the fastest and most recent algorithms to estimate Maxent models. It offers a sequential-update algorithm which is particularly efficient on sparse data and allows the addition of L1-Regularization to better control generalization performance. 
     AdaBoost, which is large margin classifier and implicitly regularized, and Maxent, which is explicitly regularized, have also been shown, both in theory and experimentally, to generalize well in the presence of a large number of features. Regularization favors simple model by penalizing large or non-zero parameters. This property allows the generalization error, i.e. the error on test data, to be bounded by quantities which are nearly independent of the number of features, both in the case of AdaBoost and Maxent. This is why a large number of features, and consequently a large number of classifier parameters, do not cause the type of overfitting (i.e. learning by heart the training data) that used to be a major problem with traditional classifiers. 
     All of the above algorithms have shown excellent performance on large scale problems. Adaboost and Maxent are particularly efficient when the data is sparse, that is when the proportion of non-zero input features is small. Their learning scales linearly as a function of the number of training samples N. Support Vector Machines, whose classification capacity is more powerful, has a learning time in theory slower, with a learning time that scales quadratically in N. A recent breakthrough has considerably improved SVM speed on sparse data. (See; Haffner, Fast transpose methods for sparse kernel learning, Presented at NIPS &#39;05 workshop on Large Scale Kernel Machines, 2005). 
     However, these computationally expensive learning algorithms cannot always handle the very large number of examples, features, and classes present in the training corpora that are available. For example, in large scale natural language processing, network monitoring, and mining applications, data available to train these algorithms can represent millions of examples and thousands of classes. With this amount of data, learning times may become excessive. 
     Furthermore, large margin classifiers were initially demonstrated on binary classification problems, where the definition of the margin is unambiguous and its impact on generalization is reasonably well understood. The simplest multiclass classification scheme is to train C binary classifiers, each trained to distinguish the examples belonging to one class from examples not belonging to this class. The situation where each example can only belong to one class leads to training one class versus all other classes. That is why this scheme is referred to as 1-vs-other or 1-vs-all. However, many other combinations of binary classifiers are possible, in particular 1-vs-1 (also called all-vs-all) where each classifier is trained to separate a pair of classes. 
     Current methods used to improve learning speed focus on a single binary classifier. However, these current methods do not guarantee a global optimal solution for different classifiers. Furthermore, current runtimes are unacceptable when processing a large number of examples with numerous classes. The run time of a deployed system should be on the order of milliseconds. A manageable learning time should be in the order of hours, with memory requirements not exceeding a few gigabytes. To remain within these constraints, one is often led to choose sub-optimal solutions. 
     Therefore, there is a need in the art for methods in which learning times are improved for large margin classifiers. Such methods may be used in interactive voice response systems or other systems requiring the handling of a large number of examples or features. 
     SUMMARY 
     Aspects of the present disclosure overcome problems and limitations of the prior art by providing methods for increasing learning speed of large margin classifiers. A first set of methods in accordance with various aspects of the invention consists of identifying processing steps that may be shared between all binary classifiers. These methods may preprocess training data or examples before classification. A second set of methods in accordance with various aspects of the invention focus on parallelization of the large margin classifiers. In an aspect of the invention, the parallelization of the large margin classifiers does not require communication between computing units, and are suitable for loosely connected networks such as described in  FIG. 2 . These methods are applicable to multiclass models where the training process may be split into training of independent binary classifiers. 
     The details of these and other embodiments of the present disclosure are set forth in the accompanying drawings and the description below. Other features and advantages of the disclosure will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure may take physical form in certain parts and steps, embodiments of which will be described in detail in the following description and illustrated in the accompanying drawings that form a part hereof, wherein: 
         FIG. 1  illustrates large margin separation between two classes in accordance with an embodiment of the disclosure. 
         FIG. 2  illustrates a diagram of a computer system that may be used to implement various aspects of the disclosure. 
         FIG. 3  illustrates a flow diagram for preprocessing data to increase learning speed for multiclass classifications in accordance with an aspect of the disclosure. 
         FIG. 4  illustrates a flow diagram for increasing learning speed for multiclass classifications using parallelization in accordance with an aspect of the disclosure. 
         FIG. 5  illustrates another flow diagram for increasing learning speed for multiclass classifications using parallelization in accordance with an aspect of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary Operating Environment 
       FIG. 2  shows a diagram of a computer system and network that may be used to implement aspects of the disclosure. A plurality of computers, such as workstations  102  and  104 , may be coupled to a computer  112 , via a network  108 ,  128 , and  118 . Computers  112 ,  114 , and  116  may be coupled to a network  128  through network  118 . Computers  112 ,  114 , and  116 , along with workstations  102  and  104  may be used for the splitting of program execution among many CPUs. Those skilled in the art will realize that other special purpose and/or general purpose computer devices may also be connected to other various networks, such as the Intranet  119 , and used for parallelization. Such additional devices may include handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, networked PCs, minicomputers, mainframe computers, and the like. 
     One or more of the computers and terminals shown in  FIG. 2  may include a variety of interface units and drives for reading and writing data or files. One skilled in the art will appreciate that networks  108 ,  118  and  128  are for illustration purposes and may be replaced with fewer or additional computer networks. One or more networks may be in the form of a local area network (LAN) that has one or more of the well-known LAN topologies and may use a variety of different protocols, such as Ethernet. One or more of the networks may be in the form of a wide area network (WAN), such as the Internet. Computer devices and other devices may be connected to one or more of the networks via twisted pair wires, coaxial cable, fiber optics, radio waves or other media. 
     Exemplary Embodiments 
     In various embodiments of the disclosure, a first set of preclassification steps may be implemented prior to classification. These preclassification steps may increase classification or learning speed of any binary classifier. These preclassification steps may be duplicated for each processor and may suggest a limit to the granularity of parallelization. 
     In a first aspect of the invention, merging of identical examples may increase learning speed. As learning time scales linearly or quadratically with the number of training examples, this can result in learning time speedups which range from two to four. 
     The problem is efficient detection of similar examples. If N is the total number of examples, pairwise comparisons would require O(N2) operations, which is not acceptable in most systems. However, one may define a strict order relationship that can be used to sort the examples using the Quick Sort or the Heap Sort routines, which tend to scale as O(N log(N)). 
     The method includes defining an order relation on the input examples as shown in  FIG. 3 . In a first step  302 , an order relation is defined. An order relation is a comparison between objects, which satisfies the properties of reflexivity, antisymmetry and transitivity. Examples of order relations include “is larger than or equal to” and “is a divisor of” between integers, and the relation “is an ancestor of” between people (if we set the convention that every individual is an ancestor of itself). 
     Most often, each data sample is represented by a finite list of scalar features. For sample x, we note these features f 1 (x), . . . , f n (x), and say that x&gt;y if and only if there exists i so that for all j&lt;i, f j (x)=f j (y) and f i (x)&gt;f i (y). It is easy to show that its negative, x&lt;=y, is an order relation. 
     Once the order is defined, in a step  304  the order relation may be used to apply a sorting routine such as Quick Sort to all the training examples (complexity O(N log(N))). 
     Next in step  306 , the sorted list of the training example may be analyzed and consecutive examples which are equal may be merged together. A training example which results in the merging of N original examples must have its importance reweighed in step  308 . After these adjustments, training on the compressed set of examples produced the same results as training on the full set, while being considerably faster. 
     In another aspect of the invention, transposing the data representation may increase learning speed. The usual representation of the data is transposed. Data is represented as a set of features, each feature being a sparse list of examples (instead of a set of examples, each examples being a list of features). This transposed data representation enables considerable speedups when the data is sparse, both in the case of the SVMs and in the case of Maxent/Adaboost. 
     In another aspect of the invention, sharing cache may increase learning speed. The most efficient way to speedup SVM learning is to cache kernel products that have already been computed. (For additional information see; T. Joachims, Making large-scale SVM learning practical, In Advances in Kernel Methods—Support Vector Learning, 1998). When learning is applied to a sequence of binary classifiers (on a single processor), the cache already computed for all previously trained classifiers can be used for the current classifier, and avoid expensive recalculation or recomputation of kernel products. 
     In another aspect of the invention, parallelization may increase learning speed. The following methods allow for an improved parallelization process. For instance, multiclass implementations with independent 1-vs-other classifiers allow for an easy parallelization. These parallelization methods do not require communication between the computing units. The parallelization methods are applicable to multiclass models where the training process may be split into the training of independent classifiers. In an exemplary method illustrated in  FIG. 4 , the classes are sorted by their frequency distribution in step  402 . Next, in step  404  load balancing may be preformed by implementing a Round Robin on the sorted list into S groups of classes. For instance, in the case where N can be decomposed as (M+1)xS, we have 
     Classes 1, S+1, . . . , MxS+1 go to subset 1, 
     Classes 2, S+2, . . . , MxS+2 go to subset 2, 
     . . . 
     Classes S, S+S, . . . , MxS+S go to subset S. 
     The frequency distribution of each group should be similar which may lead to similar training times. Learning can then be distributed over S processors in step  406 . Those skilled in the art will realize that numerous processors may be used and that such processors may be remote and located in various different locations. 
     Next in step  408 , the results of the S learning processes are merged. The gain by parallelizing into S subsets may be up to S times faster. This parallelization technique may be used for Adaboost, SVMs and Maxent the training times for each of the class subsets were very similar. In addition, this parallelization method may also be used for 1vs-1 and hierarchical combinations of binary classifiers. However, the load balancing step  404  should take into account class sizes. 
     In another aspect of the invention, another exemplary method may be used for an improved parallelization process. In the case of SVMs, a more efficient but much harder to implement parallelization strategy is possible. A cascading approach where the training data is split into a hierarchical fashion has been proposed which may reduce the number of examples each sub-learning task had to handle. (See; Hans Peter Graff, Eric Cosatto, Leon Bottou, and Igor Durdanovic, Parallel Support Vector Machines: The Cascade SVM, In Advances in Neural Information Processing Systems 17, 2005). 
     The following method implements a unique new version of this cascading approach, which is refereed to as splitting. In this method in a first step, a split may be made along examples. Then in a second step, a split may be made along classes. This method is illustrated in  FIG. 5 . In the first split along examples, the training set may be split into S subsets of equal size as shown in step  502 . Next in step  504 , each S processor may learn all the classifiers on one of the subsets. To learn all the classifiers using the same processor allows optimization of kernel cache sharing between classifiers. Gain by paralyzing into S subsets may be up to S 2  faster. In step  506 , if there is enough memory available, kernels which have been computed for the learning of one classifier can be used for the next classifiers to be learned. Next in step  508 , merge the S sets of support vectors to from the training set for the complete SVM. Finally, the reduced training set may be used to retrain the complete SVM by sorting along the classes. As shown in  FIG. 5 , the classes are sorted their frequency distribution in step  510 . Next, in step  512  load balancing may be preformed by implementing a round robin on the sorted list into S groups of classes. Learning can then be distributed over S processors in step  514 . Finally, in step  516  the results of the S learning processes may be merged. 
     In another aspect of the invention, parallelization may also be applied at runtime. Runtime as those skilled in the art will realize means use of the learning algorithm in the field. Resources are consumed in three basic operations which involve 1) Loading the parameters of the model (network and disk resources); 2) Loading and preprocessing of the data element to be classified (network and CPU); and 3) Applying the model to the preprocessed data (CPU resources). 
     Parallelization may be applied to 3) above, assuming that one has separate binary classifiers. It shall be noted that 2) above may be very time consuming and is shared between all the binary classifiers. Parallelization would require to duplicate 2) above on several processors, and if applied on a very fine grain, may result in a waste of resources. 
     In order to avoid wasted resources in an embodiment of the invention, one can select S sets so that the overheard induced is not too heavy. Next, the classifiers may be split into S sets, preferably using the same round-robin partition as during training Finally, the runtime procedure may be applied to each of the processors. 
     While the invention has been described with respect to specific examples including presently preferred modes of carrying out the invention, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and techniques that fall within the spirit and scope of the invention.
The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization through the use of multiple processors may improve learning speed. The disclosure describes effective systems for distributed multiclass classification learning on several processors. These systems are applicable to multiclass models where the training process may be split into training of independent binary classifiers.