Prestructuring Neural Networks via Extended Dependency Analysis with Application to Pattern ClassificationProc. SPIE 3722, Applications and Science of Computational Intelligence II, 402
Document TypeConference Proceeding
- Neural networks (Computer science),
- Neural networks -- Structure,
- System theory,
- Fourier transformations,
- Pattern recognition
AbstractWe consider the problem of matching domain-specific statistical structure to neural-network (NN) architecture. In past work we have considered this problem in the function approximation context; here we consider the pattern classification context. General Systems Methodology tools for finding problem-domain structure suffer exponential scaling of computation with respect to the number of variables considered. Therefore we introduce the use of Extended Dependency Analysis (EDA), which scales only polynomially in the number of variables, for the desired analysis. Based on EDA, we demonstrate a number of NN pre-structuring techniques applicable for building neural classifiers. An example is provided in which EDA results in significant dimension reduction of the input space, as well as capability for direct design of an NN classifier.
Citation InformationGeorge G. Lendaris ; Thaddeus T. Shannon ; Martin Zwick; Prestructuring neural networks via extended dependency analysis with application to pattern classification. Proc. SPIE 3722, Applications and Science of Computational Intelligence II, 402 (March 22, 1999). Copyright 1999 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/doi:10.1117/12.342895.