摘要：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.
关键词：Support vector machine classification;Learning rate;Reproducing kernel Hilbert spaces;Cesaro means
