A Radial Basis Function Approach to Pattern Recognition and Its Applications

  • Shin, Mi-Young (Electronics and Telecommunication Research Institute) ;
  • Park, Chee-Hang (Information Support Division, Electronics and Telecommunications Research Institute)
  • 투고 : 1999.08.28
  • 발행 : 2000.06.30

초록

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 (${\delta}$). 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.

키워드

참고문헌

  1. Neural Networks for Pattern Recognition Bishop, C.M.
  2. Pattern Recognition and Neural Networks Ripley, B.D.
  3. Biological Cybernerics v.63 Networks and the Best Approximation Property Girosi, F.;Poggio, T.
  4. Advance in Numerical Analysis, Wavelets, Subdivision Algorithms and Radial Basis Functions v.2 The Theory of Radial Basis Function Approaximation in 1990 Powell, M.J.D.;Light, W.A.(ed.)
  5. Data Mining and Knowledge Discovery v.1 On Bias, Variance, 0/1-loss and the Curse-of-dimensionality Friedman, J.H.
  6. Neural Computation v.4 Neural Networks and the Bias/Variance Deilemma Geman, S.;BIenenstock, E.;Doursat, R.
  7. IEEE Transactions on Neural Networks v.2 no.2 Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks Chen, S.;Cowan, C.F.N.;Grant, P.M.
  8. Neural Computation v.1 Fast Learming in Networks of Locally-tuned Processing Units Moody, J.;Darken, C.J.
  9. Design and Evaluation of Radial Basis Function Model for Function Approximation Shin, M.
  10. COLT 97 Nashville Estimation of Time-Varying Parameters in Statistical Models; an Optimization Approach Bertsimas, D.;Gamarnik, D.;Tsitsiklis, J.N.
  11. Matrix Computations Golub, G.H.;Van Loan, C.F.
  12. PROBEN1-A Set on Neural Network Benchmark Problems and Benchmarking Rules, Technical Reports 21/94 Prechelt, L.
  13. Proceedings of the International Conference on Computational Intelligence for Modeling, Control and Automation An RBF Classifier Based Framework for Software Quality Evaluation Shin, M.;Goel, A.L.
  14. Proceedings of Data Mining and Knowledge Discovery: Theory, Tools and Technology Knowledge Discovery and Validation in Software Metrics Databases Shin, M.;Goel, A.L.
  15. Statistical Learning Theory Vapnik, V.N.