Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces
نویسندگان
چکیده
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.
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ورودعنوان ژورنال:
- JCP
دوره 7 شماره
صفحات -
تاریخ انتشار 2012