Generalization Bounds for Learning the Kernel: Rademacher Chaos Complexity
نویسنده
چکیده
One of the central issues in kernel methods [5] is the problem of kernel selection (learning). This problem has recently received considerable attention which can range from the width parameter selection of Gaussian kernels to obtaining an optimal linear combination from a set of finite candidate kernels, see [3, 4]. In the latter case, kernel learning problem is often termed multi-kernel learning (MKL) in Machine Learning and nonparametric group lasso in Statistics.
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تاریخ انتشار 2009