WEIGHTED MANIFOLD MULTI-PLANE TWIN LEAST SQUARES CLASSIFICATION

摘要：

In this paper, we propose weighted Manifold Multiplane Twin Least Squares classifier which concerns the local geometry of the samples by a manifold regularization term and measures the importance degree of each sample using K neighboring relation. In addition to keeping the respective advantages of TWSVM, LSTSVM and some graph learning algorithms, our methods improve the separation of the points sharing different classes.Also experimental evidence suggests that our methods are effective in performing classification task.

关键词： TWSVM LSTSVM weighted manifold local geometry

作者: XU BO GU WEI-JIANG

作者单位: School of Information and Network Center,Nanjing ForestryUniversity,Nanjing,P. R. China School of Information Science and Technology,Nanjing Forestry University,Nanjing,P. R. China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer Technology and Development(2011第三届计算机技术与发展国际会议 ICCTD2011)

会议地点: 成都

会议语种:英文

页码: 1416-1420

在线出版日期: 2011-11-25（万方平台首次上网日期，不代表论文的发表时间）