Open AccessA Radial Basis Function Approach to Pattern Recognition and Its Applications
Author(s) -
Shin Miyoung,
Park Cheehang
Publication year - 2000
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.00.0100.0201
Subject(s) - generalization , radial basis function , interpolation (computer graphics) , computer science , basis (linear algebra) , process (computing) , artificial intelligence , algorithm , function (biology) , machine learning , hierarchical rbf , pattern recognition (psychology) , data mining , artificial neural network , mathematics , motion (physics) , mathematical analysis , geometry , evolutionary biology , biology , operating system
Pattern recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. This paper introduces a new approach, called the Representational Capability (RC) algorithm, to handle pattern recognition problems using radial basis function (RBF) models. The RC algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. The model development process based on this algorithm not only yields the best model in the sense of balancing its parsimony and generalization ability, but also provides insights into the design process by employing a design parameter ( δ ). We discuss the RC algorithm and its use at length via an illustrative example. In addition, RBF classification models are developed for heart disease diagnosis.