plonski@primrose.Aero.Org (Mike Plonski) (09/08/89)
Has anyone implemented any of Gile's work on invariant pattern recognition using higher order nets. I was able to get similiar results to Gile's (3rd ref.) for the small 2D patterns (3 by 5 input field) that he used, but I found that it was also possible to converge to spurious states. I also found that when I increase the input field to 16x16 pixels, I would always converge to a spurious state. I don't think that there is a coding problem,since I get similiar results for the smaller input field. Has anyone else experimented with this and would like to share their comments. Thanks. %A C. L. Giles %A T. Maxwell %T Learning, invariance, and generalization in high order neural networks %J Applied Optics %V 26 %N 23 %D 1 |DEC| 1987 %P 4972-4978 %A C. L. Giles %A R. D. Griffin %A T. Maxwell %T Encoding Geometric Invariances in Higher Order Neural Networks %J Neural Information Processing Systems %I AIP Conf. Proc. %E D. Z. Anderson %D 1988 %P 301-309 %A T. Maxwell %A C. L. Giles %A Y. C. Lee %A H. H. Chen %T Transformation Invariance Using Higher Order Correlations in Neural Net Architectures %J Proc. IEEE Intl. Conf. on Systems, Man, and Cybernetics %C Atlanta, GA %D 14-17 |OCT| 1986 %P 627-632 %K HOLU, group theory, tolerance, temporal invariance ------------------------------------------------------------------------------- . . .__. The opinions expressed herin are soley |\./| !__! Michael Plonski those of the author and do not represent | | | "plonski@aero.org" those of The Aerospace Corporation. _______________________________________________________________________________ We have been having some problems with our net connection lately so, if mail bounces back, then try "@brl.mil:plonski@aero.org"