Author manuscript, published in "20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges: Belgique (2012)" Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm
Year: 2012
Type: article
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
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2405065448