Local Class Boundaries for Support Vector Machine

摘要：

The support vector machine (SVM) has proved effective in classification.However,SVM easily becomes intractable in its memory and time requirements to deal with the large data, and also can not nicely deal with noisy, sparse, and imbalanced data. To overcome these issues, this paper presents a new local support vector machine that first finds k nearest neighbors from each class respectively for the query sample and then SVM is trained locally on all these selected nearest neighbors to perform the classification. This approach is efficient,sirnple and easy to implement. The conducted experiments on challenging benchmark data sets validate the proposed approach in terms of classification accuracy and robustness.

作者: Guihua Wen Caihui Zhou Jia Wei Lijun Jiang

作者单位: South China University of Technology,Guangzhou 510641, China

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 225-233

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）