Complexity bounds of radial basis functions and multi-objective learning

In the paper, the problem of multi-objective (MOBJ) learning is discussed. The problem of obtaining apparent (effective) complexity measure, which is one of the objectives, is considered. For the specific case of RBFN, the bounds on the smoothness-based complexity measure are proposed. As shown in the experimental part, the bounds can be used for Pareto set approximation.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Illya Kokshenev, Antônio de Pádua Braga
• Keywords: Complexity Measure, ESANN 2007, Neural Networks, Pareto Set Approximation, Smoothness-based Complexity Measure