Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409128 | Neurocomputing | 2008 | 7 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Illya Kokshenev, Antonio Padua Braga,