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A greedy algorithm for optimizing the kernel alignment and the performance of kernel machines

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Abstract

Kernel-target alignment has recently been proposed as a criterion for measuring the degree of agreement between a reproducing kernel and a learning task. It makes possible to find a powerful kernel for a given classification problem without designing any classifier. In this paper, we present an alternating optimization strategy, based on a greedy algorithm for maximizing the alignment over linear combinations of kernels, and a gradient descent to adjust the free parameters of each kernel. Experimental results show an improvement in the classification performance of support vector machines, and a drastic reduction in the training time.
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Dates and versions

hal-02284284 , version 1 (11-09-2019)

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  • HAL Id : hal-02284284 , version 1

Cite

Jean-Baptiste Pothin, Cédric Richard. A greedy algorithm for optimizing the kernel alignment and the performance of kernel machines. 2006 14th European Signal Processing Conference, Sep 2006, Florence, Italy. ⟨hal-02284284⟩
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