文献类型：会议论文

英文题名：Error analysis of classifiers in machine learning

作者：Ding, Lei[1]; Sheng, Bao-Huai[1]

机构：[1] Department of Mathematics, Shaoxing University, Shaoxing, Zhejiang, 315211, China

会议论文集：Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

会议日期：16 October 2010 through 18 October 2010

会议地点：Yantai

语种：英文

外文关键词：Error analysis - Polynomial approximation - Support vector machines - Vector spaces

外文摘要：The paper is related to the error analysis of Support Vector Machine (SVM) classifiers based on reproducing kernel Hilbert spaces. We choose the polynomial kernels as the Mercer kernel and give the error estimate with De La Vallée Poussin means which improve the approximation error. On the other hand, the distortion is replaced by the uniformly boundedness of the Cesàro means. We also introduce the standard estimation of the sample error, and derive the explicit learning rate. ?2010 IEEE.