2018
DOI: 10.48550/arxiv.1810.03817
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Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels

Shahin Shahrampour,
Vahid Tarokh

Abstract: Nonlinear kernels can be approximated using finite-dimensional feature maps for efficient risk minimization. Due to the inherent trade-off between the dimension of the (mapped) feature space and the approximation accuracy, the key problem is to identify promising (explicit) features leading to a satisfactory out-of-sample performance. In this work, we tackle this problem by efficiently choosing such features from multiple kernels in a greedy fashion. Our method sequentially selects these explicit features from… Show more

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