发明授权
US07519563B1 Optimizing subset selection to facilitate parallel training of support vector machines 有权
优化子集选择以促进支持向量机的并行训练

Optimizing subset selection to facilitate parallel training of support vector machines
摘要:
One embodiment of the present invention provides a system that optimizes subset selection to facilitate parallel training of a support vector machine (SVM). During operation, the system receives a dataset comprised of data points. Next, the system evaluates the data points to produce a class separability measure, and uses the class separability measure to partition the data points in the dataset into N batches. The system then performs SVM training computations on the N batches in parallel to produce support vectors for each of the N batches. Finally, the system performs a final SVM training computation using an agglomeration of support vectors computed for each of the N batches to obtain a substantially optimal solution to the SVM training problem for the entire dataset.
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