Sparse multikernel support vector regression machines trained by active learning

Authors:
V. Ceperic;G. Gielen;A. Baric
Affiliations:
Department of Electrotechnical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, Belgium and Faculty of Electrical Engineering and Computing, University of Zagreb, Unsk ...;Department of Electrotechnical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, Belgium;Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

A method for the sparse multikernel support vector regression machines is presented. The proposed method achieves a high accuracy versus complexity ratio and allows the user to adjust the complexity of the resulting models. The sparse representation is guaranteed by limiting the number of training data points for the support vector regression method. Each training data point is selected based on its influence on the accuracy of the model using the active learning principle. A different kernel function is attributed to each training data point, yielding multikernel regressor. The advantages of the proposed method are illustrated on several examples and the experiments show the advantages of the proposed method.