¹Department of Mathematics, Shaoxing College of Arts and Sciences Shaoxing, Zhejiang 312000, China
²School of Mathematics and LPMC, Nankai University,Tianjin 300071, China

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Abstract—We study the error performances of p -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.

Index Terms—Support vector machine classification, Learning rate, Reproducing kernel Hilbert spaces; Cesaro means.

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Cite: Bao-Huai Sheng, Pei-Xin Ye, "Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces," Journal of Computers vol. 7, no. 1, pp. 252-257, 2012.