Patent ID: 7519563

Claim:
A method for optimizing subset selection to facilitate parallel training of a support vector machine (SVM), comprising: receiving a dataset comprised of data points; evaluating the data points to produce a class separability measure; using the class separability measure to partition the data points in the dataset into N subsets, wherein the class separability measure J represents the ratio of traces between class scatter matrices tr(S B ); and traces within class scatter matrices tr(S W ); wherein J = tr ⁡ ( S B ) tr ⁡ ( S W ) ; using two or more processors in a multiprocessor system in parallel to perform separate SVM training computation for each subset in the N subsets to produce a different set of support vectors for each of the N subsets, wherein each subset contains a separate portion of data points of the entire dataset; and performing a final SVM training computation using an agglomeration of different sets of support vectors computed for each of the N subsets to obtain a substantially optimal solution to the SVM training problem for the entire dataset.