Weighted smooth CHKS twin support vector machines

被引：5

作者：

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China ^{[1
]}

不详 ^{[2
]}

机构：

[1] School of Computer Science and Technology, China University of Mining and Technology

[2] Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, The Chinese Academy of Sciences

来源：

Huang, H.-J. (hhj-025@163.com) | 1600年 / Chinese Academy of Sciences卷 / 24期

关键词：

CHKS function; Smooth; Smooth twin support vector machines; Twin support vector machines; Weight;

D O I：

10.3724/SP.J.1001.2013.04475

中图分类号：

学科分类号：

摘要：

Smooth twin support vector machines (STWSVM) uses Sigmoid function to transform the unsmooth twin support vector machines (TWSVM) into smooth ones. However, because of the low approximation ability of Sigmoid function, the classification accuracy of STWSVM is unsatisfactory. Furthermore, similar to TWSVM, STWSVM is sensitive to the abnormal samples. In order to address the above problems, this paper introduces CHKS function, and proposes a smooth twin support vector machines, smooth CHKS twin support vector machines (SCTWSVM). In order to reduce the influence of abnormal samples on SCTWSVM, different importance are given for each training sample according to the sample point positions for SCTWSVM, resulting in weighted smooth CHKS twin support vector machines (WSCTWSVM). The study proves that SCTWSVM is not only strictly convex, but also can meet the arbitrary order smooth performance. Meanwhile, the experimental results show that SCTWSVM has better performance than STWSVM. Furthermore, the experimental results also show that WSCTWSVM is effective and feasible relative to SCTWSVM. ©Copyright 2013, Institute of Software, the Chinese Academy of Sciences.

引用

页码：2548 / 2557

页数：9

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