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Optimal Feature Representation for Kernel Machines using Kernel-Target Alignment Criterion

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Abstract

Kernel-target alignment is commonly used to predict the behavior of any given reproducing kernel in a classification context, without training any kernel machine. In this paper, we present a gradient ascent algorithm for maximizing the alignment over linear transform of the input space. Our method is compared to the minimization of the radius-margin bound. Experimental results on multi-dimensional benchmarks show the effectiveness of our approach.
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hal-02356442 , version 1 (08-11-2019)

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Jean-Baptiste Pothin, Cédric Richard. Optimal Feature Representation for Kernel Machines using Kernel-Target Alignment Criterion. 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr 2007, Honolulu, United States. pp.III-1065-III-1068, ⟨10.1109/ICASSP.2007.366867⟩. ⟨hal-02356442⟩
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