%0 Journal Article
%T Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces
%A Bao-Huai Sheng
%A Pei-Xin Ye
%J Journal of Computers
%D 2012
%I Academy Publisher
%R 10.4304/jcp.7.1.252-257
%X We study the error performances of -norm Support Vector Machine classifiers based on reproducing kernel Hilbert spaces. We focus on two category problem and choose the data-dependent polynomial kernels as the Mercer kernel to improve the approximation error. We also provide the standard estimation of the sample error, and derive the explicit learning rate.
%K Support vector machine classification
%K Learning rate
%K Reproducing kernel Hilbert spaces
%K Cesaro means
%U http://ojs.academypublisher.com/index.php/jcp/article/view/4729