A multi-objective approach to RBF network learning

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. r 2008 Elsevier B.V. All rights reserved.

• Type: Journal
• Year: 2008
• Where: IJON
• Authors: Illya Kokshenev, Antônio de Pádua Braga
• Keywords: Complexity Measure, IJON 2006, IJON 2008, RBF Network, Supervised Learning