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• W2974617835 abstract "In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regularization. We show that the solution admits a multi-kernel expansion with adaptive positions. In this representation, the number of active kernels is upper-bounded by the number of data points while the gTV regularization imposes an $ell_1$ penalty on the kernel coefficients. Finally, we illustrate numerically the outcome of our theory." @default.
• W2974617835 created "2019-09-26" @default.
• W2974617835 creator A5027830035 @default.
• W2974617835 creator A5031317746 @default.
• W2974617835 date "2018-11-02" @default.
• W2974617835 modified "2023-10-01" @default.
• W2974617835 title "Multi-Kernel Regression with Sparsity Constraint" @default.
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• W2974617835 hasPublicationYear "2018" @default.
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