Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm

Abstract : We propose a novel algorithm for learning a geometric com- bination of Gaussian kernel jointly with a SVM classifier. This problem is the product counterpart of MKL, with restriction to Gaussian kernels. Our algorithm finds a local solution by alternating a Quasi-Newton gradi- ent descent over the kernels and a classical SVM solver over the instances. We show promising results on well known data sets which suggest the soundness of the approach.
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Submitted on : Thursday, June 7, 2012 - 3:10:34 PM
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David Picard, Nicolas Thome, Matthieu Cord, Alain Rakotomamonjy. Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm. 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2012, Bruges, Belgium. pp.79-84. ⟨hal-00705374⟩

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