A multi-objective approach to RBF network learning

Authors:
Illya Kokshenev;Antonio Padua Braga
Affiliations:
Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6.627-Campus UFMG Pampulha 30.161-970, Belo Horizonte, MG, Brazil;Depto. Engenharia Eletrônica, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6.627-Campus UFMG Pampulha 30.161-970, Belo Horizonte, MG, Brazil
Venue:
Neurocomputing
Year:
2008

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Abstract

The problem of inductive supervised learning is discussed in this paper within the context of multi-objective (MOBJ) optimization. The smoothness-based apparent (effective) complexity measure for RBF networks is considered. For the specific case of RBF network, bounds on the complexity measure are formally described. As the synthetic and real-world data experiments show, the proposed MOBJ learning method is capable of efficient generalization control along with network size reduction.