Sparse Bayesian modeling with adaptive kernel learning

Authors:
Dimitris G. Tzikas;Aristidis C. Likas;Nikolaos P. Galatsanos
Affiliations:
Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Computer Science, University of Ioannina, Ioannina, Greece;Department of Electrical and Computer Engineering, University of Patras, Rio, Greece
Venue:
IEEE Transactions on Neural Networks
Year:
2009

Citing 11
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Abstract

Sparse kernel methods are very efficient in solving regression and classification problems. The sparsity and performance of these methods depend on selecting an appropriate kernel function, which is typically achieved using a cross-validation procedure. In this paper, we propose an incremental method for supervised learning, which is similar to the relevance vector machine (RVM) but also learns the parameters of the kernels during model training. Specifically, we learn different parameter values for each kernel, resulting in a very flexible model. In order to avoid over-fitting, we use a sparsity enforcing prior that controls the effective number of parameters of the model. We present experimental results on artificial data to demonstrate the advantages of the proposed method and we provide a comparison with the typical RVM on several commonly used regression and classification data sets.