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.
complexity generalization multi-objective learning regularization radial basis functions