Multi-class classification learning on several processors
First Claim
Patent Images
1. An interactive voice response system comprising:
- a first computing unit configured to;
receive a training data set;
sort classes of the training data set by a frequency distribution to yield sorted classes; and
distribute the sorted classes as a plurality of groups across a plurality of 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 processors, wherein each of the processors is located within a different computing unit, and each processor is configured to process the distributed group of sorted classes to produce learning data and distribute the learning data to each of the other processors;
a second computing unit configured to merge results of the processing into a model; and
a third computing unit configured to receive the model and apply the model.
5 Assignments
0 Petitions
Accused Products
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 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.
17 Citations
15 Claims
-
1. An interactive voice response system comprising:
-
a first computing unit configured to; receive a training data set; sort classes of the training data set by a frequency distribution to yield sorted classes; and distribute the sorted classes as a plurality of groups across a plurality of 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 processors, wherein each of the processors is located within a different computing unit, and each processor is configured to process the distributed group of sorted classes to produce learning data and distribute the learning data to each of the other processors; a second computing unit configured to merge results of the processing into a model; and a third computing unit configured to receive the model and apply the model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. An interactive voice response system comprising:
-
a first computing unit configured to; receive a training data set; split the training data set along examples to yield a split training data set along examples; split the split training data set along classes to yield a split training data set along classes; separate the split training data set along classes as a training set into subsets of equal size; and distribute the subsets in across a plurality of processors, such that one subset is distributed to one processor to yield a distributed subset, wherein each of the plurality of processors is located within a different computing unit, and each processor is configured to determine all classifiers of a distributed subset; a second computing unit configured to merge results of the determining into a model and output the model to cache operatively connected to the second computing unit; and a third computing unit configured to receive the model from the cache and apply the model. - View Dependent Claims (11, 12, 13, 14, 15)
-
Specification