Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653588 | Neurocomputing | 2005 | 5 Pages |
Abstract
This letter proposes a constructive training method for radial basis function networks. The proposed method is an extension of the dynamic decay adjustment (DDA) algorithm, a fast constructive algorithm for classification problems. The proposed method, which is based on selective pruning and DDA model selection, aims to improve the generalization performance of DDA without generating larger networks. Simulations using four image recognition datasets from the UCI repository demonstrate the validity of the proposed method.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Adriano L.I. Oliveira, Bruno J.M. Melo, Silvio R.L. Meira,