A parallel decomposition algorithm for training multiclass kernel-based vector machines
Niu, Lingfeng1,2; Yuan, Ya-Xiang1
刊名OPTIMIZATION METHODS & SOFTWARE
2011
卷号26期号:3页码:431-454
关键词Kernel-based vector machines decomposition method projected gradient parallel algorithm
ISSN号1055-6788
DOI10.1080/10556788.2011.556633
英文摘要We present a decomposition method for training Crammer and Singer's multiclass kernel-based vector machine model. A new working set selection rule is proposed. Global convergence of the algorithm based on this selection rule is established. Projected gradient method is chosen to solve the resulting quadratic subproblem at each iteration. An efficient projection algorithm is designed by exploiting the structure of the constraints. Parallel strategies are given to utilize the storage and computational resources available on the multiprocessor system. Numerical experiment on benchmark problems demonstrates that the good classification accuracy and remarkable time saving can be achieved.
资助项目NSFC[11026187] ; NSFC[10831006] ; NSFC[70921061] ; CAS[kjcx-yw-s7]
WOS研究方向Computer Science ; Operations Research & Management Science ; Mathematics
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000299550000009
内容类型期刊论文
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/11306]  
专题计算数学与科学工程计算研究所
通讯作者Yuan, Ya-Xiang
作者单位1.Chinese Acad Sci, State Key Lab Sci & Engn Comp, Inst Computat Math & Sci Engn Comp, AMSS, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Grad Univ, Chinese Acad Sci,AMSS, Beijing 100190, Peoples R China
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Niu, Lingfeng,Yuan, Ya-Xiang. A parallel decomposition algorithm for training multiclass kernel-based vector machines[J]. OPTIMIZATION METHODS & SOFTWARE,2011,26(3):431-454.
APA Niu, Lingfeng,&Yuan, Ya-Xiang.(2011).A parallel decomposition algorithm for training multiclass kernel-based vector machines.OPTIMIZATION METHODS & SOFTWARE,26(3),431-454.
MLA Niu, Lingfeng,et al."A parallel decomposition algorithm for training multiclass kernel-based vector machines".OPTIMIZATION METHODS & SOFTWARE 26.3(2011):431-454.
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