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    • 20. 发明授权
    • Optimizing subset selection to facilitate parallel training of support vector machines
    • 优化子集选择以促进支持向量机的并行训练
    • US07519563B1
    • 2009-04-14
    • US11053385
    • 2005-02-07
    • Aleksey M. UrmanovAnton A. BougaevKenny C. Gross
    • Aleksey M. UrmanovAnton A. BougaevKenny C. Gross
    • G06F15/18G05B13/02
    • G06K9/6269G06N99/005
    • 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.
    • 本发明的一个实施例提供一种优化子集选择以促进支持向量机(SVM)的并行训练的系统。 在操作期间,系统接收由数据点组成的数据集。 接下来,系统评估数据点以产生类可分离性度量,并使用类可分离性度量将数据集中的数据点分解成N个批次。 然后,系统对N个批次并行执行SVM训练计算,以产生每个N个批次的支持向量。 最后,系统使用针对N个批次中的每一个计算的支持向量的聚集来执行最终SVM训练计算,以获得对于整个数据集的SVM训练问题的基本上最佳的解决方案。