Hyperbolic Tangent Support Vector Machine |
| |
Authors: | LIU Ye-qing LIU San-yang GU Ming-tao |
| |
Institution: | 1 School of Science,Xidian University,Xi'an 710071,China2 School of Science,Henan University of Science & Technology,Luoyang 471003,China3 96251 PLA Troops,Luoyang 471003,China |
| |
Abstract: | By utilizing hyperbolic tangent function,we constructed a novel hyperbolic tangent loss function to reduce the influences of outliers on support vector machine (SVM) classification problem.The new lass fuinction not only limits the maximal loss value of outliers but also is smooth.Hyperbolic tangent SVM (HTSVM) is then proposed based on the new loss function.The experimental results show that HTSVM reduces the effects of outliers and gives better generalization performance than the classical SVM on both artificial data and UCI data sets.Therefore,the proposed hyperbolic tangent loss faction and HTSVM are both effective. |
| |
Keywords: | support vector machine (SVM) classification pattern recognition |
本文献已被 CNKI 维普 万方数据 等数据库收录! |
|