Novel probabilistic neuroclassifier

Jiang Hong; Gursel Serpen

doi:10.1117/12.522206

2 September 2003 Novel probabilistic neuroclassifier

Jiang Hong, Gursel Serpen

Proceedings Volume 5253, Fifth International Symposium on Instrumentation and Control Technology; (2003) https://doi.org/10.1117/12.522206
Event: Fifth International Symposium on Instrumentation and Control Technology, 2003, Beijing, China

Abstract

A novel probabilistic potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in this paper. The proposed classifier has a number of desirable properties which distinguish it from other neural network classifiers. A complete description of the algorithm in terms of its architecture and the pseudocode is presented. Simulation analysis of the newly proposed neuro-classifier algorithm on a set of benchmark problems is presented. Benchmark problems tested include IRIS, Sonar, Vowel Recognition, Two-Spiral, Wisconsin Breast Cancer, Cleveland Heart Disease and Thyroid Gland Disease. Simulation results indicate that the proposed neuro-classifier performs consistently better for a subset of problems for which other neural classifiers perform relatively poorly.

Citation Download Citation

Jiang Hong and Gursel Serpen "Novel probabilistic neuroclassifier", Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); https://doi.org/10.1117/12.522206

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