Methods to distribute multi-class classification learning on several processors
First Claim
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1. A method for applying a model for an interactive voice response system comprising:
- a) receiving a training data set at a first computing unit;
b) sorting classes of the training data set by frequency distribution at the first computing unit;
c) distributing the sorted classes as a plurality of S groups across a plurality of S processors using a round robin partition, wherein each group includes classes different from classes in each other group, and each group is distributed to a different processor of the plurality of S processors, each of the S processors being located within a different computing unit;
d) for each processor, processing the distributed group of sorted classes to produce learning data;
e) for each processor, distributing the learning data to each of the other processors;
f) merging results of the processing into a model at a second computing unit; and
g) outputting the model to cache operatively connected to the second computing unit; and
h) applying the model to an interactive voice response system.
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Abstract
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 thought the use of multiple processors may improve learning speed. The invention describes effective methods to distributed multiclass classification learning on several processors. These methods are applicable to multiclass models where the training process may be split into training of independent binary classifiers.
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Citations
11 Claims
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1. A method for applying a model for an interactive voice response system comprising:
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a) receiving a training data set at a first computing unit; b) sorting classes of the training data set by frequency distribution at the first computing unit; c) distributing the sorted classes as a plurality of S groups across a plurality of S processors using a round robin partition, wherein each group includes classes different from classes in each other group, and each group is distributed to a different processor of the plurality of S processors, each of the S processors being located within a different computing unit; d) for each processor, processing the distributed group of sorted classes to produce learning data; e) for each processor, distributing the learning data to each of the other processors; f) merging results of the processing into a model at a second computing unit; and g) outputting the model to cache operatively connected to the second computing unit; and h) applying the model to an interactive voice response system. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method for applying a model for an interactive voice response system comprising:
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a) receiving a training data set at a first computing unit; b) splitting the training data sets along examples at the first computing device; c) splitting the split training data sets from a) along classes at the first computing device; d) separating the split training data sets from c) as a training set S into S subsets of equal size at the first computing device; e) distributing the S subsets in d) across a plurality of S processors, wherein one subset is distributed to one processor, each of the S processors being located within a different computing unit; f) for each of the plurality of S processors, determining all the classifiers of the distributed subset; g) merging results of the processing into a model at a second computing unit; h) outputting the model to cache operatively connected to the second computing unit; and i) applying the model to an interactive voice response system. - View Dependent Claims (9, 10, 11)
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Specification