Incorporating context and geometry in kernel design for support vector machines Contexte et géométrie pour la conception des noyaux

Abstract

Kernels are functions designed in order to capture resemblance between data and they are used in a wide range of machine learning techniques including support vector networks (SVMs). In their standard version, commonly used kernels such as the Gaussian, show reasonably good performance in many classification and recognition tasks in computer vision, bio… (More)

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