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Kernel Maximum Likelihood Hebbian Learning

  • Jos Koetsier
  • Emilio Corchado
  • Donald MacDonald
  • Juan Corchado
  • Colin Fyfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3037)

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.

Keywords

Feature Space Maximum Likelihood Method Kernel Version General Cost Function Topology Preservation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jos Koetsier
    • 1
  • Emilio Corchado
    • 2
  • Donald MacDonald
    • 1
  • Juan Corchado
    • 3
  • Colin Fyfe
    • 1
  1. 1.Applied Computation Intelligence Research UnitUniversity of Paisley
  2. 2.Departamento de Ingenieria CivilUniversidad de BurgosSpain
  3. 3.Departamento de Informática y AutomáticaUniversidad de SalamancaSpain

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