Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
408394 | Neurocomputing | 2007 | 8 Pages |
Abstract
This paper presents the performance evaluation of the recently developed Growing and Pruning Radial Basis Function (GAP-RBF) algorithm for classification problems. Earlier GAP-RBF was evaluated only for function approximation problems. Improvements to GAP-RBF for enhancing its performance in both accuracy and speed are also described and the resulting algorithm is referred to as Fast GAP-RBF (FGAP-RBF). Performance comparison of FGAP-RBF algorithm with GAP-RBF and the Minimal Resource Allocation Network (MRAN) algorithm based on four benchmark classification problems, viz. Phoneme, Segment, Satimage and DNA are presented. The results indicate that FGAP-RBF produces higher classification accuracy with reduced computational complexity.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Runxuan Zhang, Guang-Bin Huang, N. Sundararajan, P. Saratchandran,