发明授权
- 专利标题: Optimizing subset selection to facilitate parallel training of support vector machines
- 专利标题(中): 优化子集选择以促进支持向量机的并行训练
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申请号: US11053385申请日: 2005-02-07
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公开(公告)号: US07519563B1公开(公告)日: 2009-04-14
- 发明人: Aleksey M. Urmanov , Anton A. Bougaev , Kenny C. Gross
- 申请人: Aleksey M. Urmanov , Anton A. Bougaev , Kenny C. Gross
- 申请人地址: US CA Santa Clara
- 专利权人: Sun Microsystems, Inc.
- 当前专利权人: Sun Microsystems, Inc.
- 当前专利权人地址: US CA Santa Clara
- 代理机构: Park, Vaughan & Fleming LLP
- 主分类号: G06F15/18
- IPC分类号: G06F15/18 ; G05B13/02
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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