发明授权
- 专利标题: Method for high-level parallelization of large scale QP optimization problems
- 专利标题(中): 高等级并行化大规模QP优化问题的方法
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申请号: US11111031申请日: 2005-04-21
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公开(公告)号: US07231376B1公开(公告)日: 2007-06-12
- 发明人: Filiz Gurtuna , Aleksey M. Urmanov , Kenny C. Gross
- 申请人: Filiz Gurtuna , Aleksey M. Urmanov , 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
- 代理商 Shun Yao
- 主分类号: G06F15/18
- IPC分类号: G06F15/18
摘要:
One embodiment of the present invention provides a system that performs high-level parallelization of large scale quadratic-problem (QP) optimization. During operation, the system receives a training dataset comprised of a number of data vectors. The system first determines to what extent each data vector violates conditions associated with a current support vector machine (SVM). The system then sorts the data vectors based on each data vector's degree of violation. Next, the system partitions the sorted data vectors into a number of prioritized subsets, wherein the subset with the highest priority contains the largest number of violators with the highest degree of violation. The system subsequently solves in parallel a QP optimization problem for each subset based on the subset's priority. The system then constructs a new SVM to replace the current SVM based on the QP optimization solution for each subset.
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