Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces

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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.