Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm
Résumé
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.
Domaines
Machine Learning [stat.ML]| Origine | Fichiers produits par l'(les) auteur(s) |
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