Sparse RBF Networks with Multi-kernels

Authors:
Lihua Fu;Meng Zhang;Hongwei Li
Affiliations:
School of Mathematics and Physics, China University of Geosciences, Wuhan, China 430074;Department of Computer Science, Central China Normal University, Wuhan, China 430079;School of Mathematics and Physics, China University of Geosciences, Wuhan, China 430074
Venue:
Neural Processing Letters
Year:
2010

Citing 18
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Abstract

While the conventional standard radial basis function (RBF) networks are based on a single kernel, in practice, it is often desirable to base the networks on combinations of multiple kernels. In this paper, a multi-kernel function is introduced by combining several kernel functions linearly. A novel RBF network with the multi-kernel is constructed to obtain a parsimonious and flexible regression model. The unknown centres of the multi-kernels are determined by an improved k-means clustering algorithm. And orthogonal least squares (OLS) algorithm is used to determine the remaining parameters. The complexity of the newly proposed algorithm is also analyzed. It is demonstrated that the new network can lead to a more parsimonious model with much better generalization property compared with the traditional RBF networks with a single kernel.