Candidate working set strategy based SMO algorithm in support vector machine

作者:

Highlights:

摘要

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.

论文关键词:Support vector machine,SMO,Candidate working set strategy,Kernel cache

论文评审过程:Received 11 February 2009, Revised 5 May 2009, Accepted 5 May 2009, Available online 2 June 2009.

论文官网地址:https://doi.org/10.1016/j.ipm.2009.05.002