Incremental Learning Method of Least Squares Support Vector Machine
As the expansion of the standard Support Vector Machine, compared with the traditional standard Support Vector Machine, the Least Squares Support Vector Machine loses the sparseness of standard Support Vector Machine, which would affect the efficiency of the second study. Aimed at the above puzzle, the article proposed an improved Least Squares Support Vector Machine incremental learning method, using self-adaptive methods to prune the sample, according to the performance of the classifier which each training has been to set the pruning threshold and the increment size of the sample, if you get a good performance of classifier, pruning threshold and sample increment is big, the other hand, if you get a poor performance of classifier, pruning threshold and sample increment is small, resulting in improved efficiency of Least Squares Support Vector Machine training to solve the sparse problem. The simulation experiment results verify the proposed algorithm is feasible.
self-adaptive methods Support Vector Machine incremental learning method pruning threshold sample incremen
Liu Yucheng Liu Yubin
College of Electronic Information Engineering Chongqing University of Science & Technology Chongqing Computer Science School Panzhihua Univerisity Panzhihua, China
国际会议
长沙
英文
1699-1702
2010-05-11（万方平台首次上网日期，不代表论文的发表时间）