Abstract
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 [2]. 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.
Chapter PDF
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Charles, D., Fyfe, C.: Modelling Multiple Cause Structure using Rectification con-Straints. Network: Computation in Neural Systems 9, 167–182 (1998)
Corchado, E., Fyfe, C.: Maximum Likelihood Hebbian Rules. In: Tenth European Symposium on Artificial Neural Networks, ESANN 2002, pp. 143–148 (2002)
Fyfe, C.P.: Properties of Interneurons. In: From Neurobiology to Real World Computing, ICANN 1993, pp. 183–188 (1993)
Fyfe, C.: Introducing Asymmetry into Interneuron learning. Neural Computation 7(6), 1167–1181 (1995)
Fyfe, C.: Radial Feature Mapping. In: International Conference on Artificial Neural Networks, ICANN 1995 (October 1995)
Fyfe, C.: A Comparative Study of Two Neural Methods of Exploratory Projection Pursuit. Neural Networks 10(2), 257–262 (1997)
Fyfe, C., Baddeley, R.: Non-linear Data Dtructure Extraction using Simple Hebbian Networks. Biological Cybernetics 72(6), 533–541 (1995)
Fyfe, C., Charles, D.: Using Noise to Form a Minimal Overcomplete Basis. In: Seventh International Conference on Artificial Neural Networks, ICANN 1999 (1999)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Koetsier, J., Corchado, E., MacDonald, D., Corchado, J., Fyfe, C. (2004). Kernel Maximum Likelihood Hebbian Learning. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science - ICCS 2004. ICCS 2004. Lecture Notes in Computer Science, vol 3037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24687-9_94
Download citation
DOI: https://doi.org/10.1007/978-3-540-24687-9_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22115-9
Online ISBN: 978-3-540-24687-9
eBook Packages: Springer Book Archive