Minimal distance neural methods

A general framework for minimal distance methods is presented. Radial basis functions (RBFs) and multilayer perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on real-world datasets obtaining very encouraging results.

[1]  Krzysztof Grabczewski,et al.  Extraction of crisp logical rules using constructive constrained backpropagation networks , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[2]  L. N. Kanal,et al.  Handbook of Statistics, Vol. 2. Classification, Pattern Recognition and Reduction of Dimensionality. , 1985 .

[3]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[4]  Patrik Floréen.  The convergence of Hamming memory networks , 1991, IEEE Trans. Neural Networks.

[5]  Sholom M. Weiss,et al.  An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods , 1989, IJCAI.

[6]  Antonio Bellacicco,et al.  Handbook of statistics 2: Classification, pattern recognition and reduction of dimensionality: P.R. KRISHNAIAH and L.N. KANAL (Eds.) North-Holland, Amsterdam, 1982, xxii + 903 pages, Dfl.275.00 , 1984 .

[7]  D. K. Y. Chiu,et al.  The ck-Nearest Neighbor Distance Network: A Network Using Class Boundary Feature Distances , 1997, ICONIP.

[8]  Włodzisław Duch,et al.  Neural minimal distance methods , 1997 .

[9]  Erkki Oja,et al.  Classification with learning k-nearest neighbors , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[10]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[11]  Wlodzislaw Duch,et al.  Feature space mapping as a universal adaptive system , 1995 .

[12]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Wlodzislaw Duch,et al.  Logical Rules for Classification of Medical Data Using Ontogenic Neural Algorithm , 1997, ICONIP.

[14]  David L. Waltz,et al.  Memory-based reasoning , 1998 .

[15]  Lester Ingber,et al.  Adaptive simulated annealing (ASA): Lessons learned , 2000, ArXiv.

[16]  Jerome H. Friedman,et al.  Flexible Metric Nearest Neighbor Classification , 1994 .

[17]  Wolfram Schiffmann,et al.  Comparison of optimized backpropagation algorithms , 1993, ESANN.

[18]  Michal Morciniec,et al.  A Theoretical and Experimental Account of n-Tuple Classifier Performance , 1996, Neural Computation.

[19]  Wlodzislaw Duch,et al.  Extraction of crisp logical rules using constrained backpropagation networks , 1997, ESANN.