Candidate working set strategy based SMO algorithm in support vector machine

Song, Xiao-Feng, Chen, Wei-Min, Chen, Yi-Ping Phoebe and Jiang, Bin 2009, Candidate working set strategy based SMO algorithm in support vector machine, Information processing & management, vol. 45, no. 5, pp. 584-592, doi: 10.1016/j.ipm.2009.05.002.

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Title Candidate working set strategy based SMO algorithm in support vector machine
Author(s) Song, Xiao-Feng
Chen, Wei-Min
Chen, Yi-Ping Phoebe
Jiang, Bin
Journal name Information processing & management
Volume number 45
Issue number 5
Start page 584
End page 592
Total pages 9
Publisher Elsevier Ltd.
Place of publication London, England
Publication date 2009
ISSN 0306-4573
Keyword(s) Support vector machine
Candidate working set strategy’
Kernel cache
Summary Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.
Language eng
DOI 10.1016/j.ipm.2009.05.002
Field of Research 080403 Data Structures
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
HERDC collection year 2009
Copyright notice ©2009, Elsevier Ltd.
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