The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Optimization of the SVM Kernels Using an Empirical Error Minimization Scheme
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Evolutionary tuning of multiple SVM parameters
Neurocomputing
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
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The task of RBF kernel selection in Relevance Vector Machines (RVM) is considered. RVM exploits a probabilistic Bayesian learning framework offering number of advantages to state-of-the-art Support Vector Machines. In particular RVM effectively avoids determination of regularization coefficient C via evidence maximization. In the paper we show that RBF kernel selection in Bayesian framework requires extension of algorithmic model. In new model integration over posterior probability becomes intractable. Therefore point estimation of posterior probability is used. In RVM evidence value is calculated via Laplace approximation. However, extended model doesn't allow maximization of posterior probability as dimension of optimization parameters space becomes too high. Hence Laplace approximation can be no more used in new model. We propose a local evidence estimation method which establishes a compromise between accuracy and stability of algorithm. In the paper we first briefly describe maximal evidence principle, present model of kernel algorithms as well as our approximations for evidence estimation, and then give results of experimental evaluation. Both classification and regression cases are considered.