In the paper we propose a method based on Bayesian frame-work for selecting the best kernel function for supervised learning problem. The parameters of the kernel function are considered as model parameters and maximum evidence principle is applied for model selection. We describe a general scheme of Bayesian regularization, present model of kernel classifiers as well as our approximations for evidence estimation, and then give some results of experimental evaluation. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Kropotov, D., Ptashko, N., & Vetrov, D. (2005). The use of Bayesian framework for kernel selection in vector machines classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3773 LNCS, pp. 252–261). https://doi.org/10.1007/11578079_27
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