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

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.