Improved Sparse Least Square Support Vector Machines for the Function Estimation

摘要：

Least square support vector machines (LS-SVMs) are deemed good methods for classification and function estimation. Compared with the standard support vector machines (SVMs), a drawback is that the sparseness is lost in the LS-SVMs. The sparseness is imposed by omitting the less important data in the training process, and retraining the remaining data. lterative retraining requires more intensive computations than training the non-sparse LS-SVMs. In this paper, we will describe a new pruning algorithm for LS-SVMs: the width of e-insensitive zone is introduced in the process of the training; in addition, the amount of the pruning points is adjusted according to the performance of training, not specified using the fixed percentage of training data; furthermore, the cross training is applied in the training. The performance of improved LS-SVMs pruning algorithm, in terms of computational cost and regress accuracy, is shown by several experiments based on the same data sets of chaotic time series.

关键词： LS-SVMs improved sparse LS-SVMs ε-insensitive zone pruning function estimation

作者: Yafeng.Zhang Songnian.Yu

作者单位: Shanghai University, ShangHai, 200072,China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 1680-1683

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）