Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm
David Picard, Nicolas Thome, Matthieu Cord, Alain Rakotomamonjy · 2012
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. 1