A REDUNDANT INCREMENTAL LEARNING ALGORITHM FOR SVM

摘要：

This paper presents an improved incremental learning technique for SVM, namely redundant incremental SVM (RISVM), for pattern classification problems. Through adding some non-support vectors (say, redundant vectors in the sense of contribution to the final solution) at each incremental step, the RISVM algorithm can achieve similar performance to the SVM in batch (or non-incremental SVM) but result in less support vectors for the same quality of pattern classification, and also it can provide better generalization performance in comparison with other incremental techniques for SVM. The bispiral problem and five widely used benchmark data sets are employed to verify the method, and the simulations support the feasibility and effectiveness of the proposed approach.

关键词： Classification Incremental learning Redundant vector Support vector machine

作者: WEN-JIAN WANG

作者单位: School of Computer and Information Technology, Key Laboratory of Computational Intelligence & Chinese Information Processing of Ministry of Education Shanxi University, Taiyuan, P.R.China 030006

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 734-738

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）