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Advisor(s)
Abstract(s)
We propose a new training algorithm for radial basics function networks (RBFN), which incorporates both generative (mixture-based) and discriminative (logistic) criteria. Our algorithm incorporates steps from the classical expectation-maximization algorithm for mixtures of Gaussians with a logistic regression step to update (in a discriminative way) the output weights. We also describe an incremental version of the algorithm, which is robust regarding initial conditions. Comparison of our approach with existing training algorithms, on (both synthetic and real) binary classification problems, shows that it achieves better performance.
Description
Keywords
radial generative discriminative
Citation
FERREIRA, Artur J.; FIGUEIREDO, Mário A. T. – Hybrid Generative/Discriminative Training of Radial Basis Function Networks. In 14th European Symposium on Artificial Neural Networks – ESANN'06. Bruges, Bélgica: dblp – computer science bibliographyr, 2006. ISBN 2-930307-06-4. Pp. 599-604.