Multi-kernel growing support vector regressor

Authors:
D. Gutiérrez-González;E. Parrado-Hernández;A. Navia-Vázquez
Affiliations:
Dept. Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Dept. Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Madrid, Spain;Dept. Signal Processing and Communications, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 5
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Abstract

This paper presents a method to iteratively grow a compact Support Vector Regressor so that the balance between size of the machine and its performance can be user-controlled. The algorithm is able to combine Gaussian kernels with different spread parameter, skipping the ‘a priori' parameter estimation by allowing a progressive incorporation of nodes with decreasing values of the spread parameter, until a cross-validation stopping criterion is met. Experimental results show the significant reduction achieved in the size of the machines trained with this new algorithm and their good generalization capabilities.