Kernel Maximum Likelihood Hebbian Learning
We present a novel method based on a recently proposed extension to a negative feedback network which uses simple Hebbian learning to self-organise called Maximum Likelihood Hebbian learning . We use the kernel version of the ML algorithm on data from a spectroscopic analysis of a stained glass rose window in a Spanish cathedral. It is hoped that in classifying the origin and date of each segment it will help in the restoration of this and other historical stain glass windows.
KeywordsFeature Space Maximum Likelihood Method Kernel Version General Cost Function Topology Preservation
- 2.Corchado, E., Fyfe, C.: Maximum Likelihood Hebbian Rules. In: Tenth European Symposium on Artificial Neural Networks, ESANN 2002, pp. 143–148 (2002)Google Scholar
- 3.Fyfe, C.P.: Properties of Interneurons. In: From Neurobiology to Real World Computing, ICANN 1993, pp. 183–188 (1993)Google Scholar
- 5.Fyfe, C.: Radial Feature Mapping. In: International Conference on Artificial Neural Networks, ICANN 1995 (October 1995)Google Scholar
- 8.Fyfe, C., Charles, D.: Using Noise to Form a Minimal Overcomplete Basis. In: Seventh International Conference on Artificial Neural Networks, ICANN 1999 (1999)Google Scholar
- 9.Lopez-Gejo, J., Colina, A., Lopez-Palacios, J., Bravo, P.: Principal Components Analysis in the Classification of Medieval Glasses by Scanning Electron Microscopy Coupled with Energy Dispersive X-ray Analysis (2003) (submitted)Google Scholar