8. 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) 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.
8. 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) 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. 11. The method of claim 8 , the method further comprises storing in cache memory previously processed classes of the training set data for each of the plurality of S processors.