کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
408394 679025 2007 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improved GAP-RBF network for classification problems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improved GAP-RBF network for classification problems
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 16–18, October 2007, Pages 3011–3018
نویسندگان
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