A Kernel Method for Classification

  • Donald MacDonald
  • Jos Koetsier
  • Emilio Corchado
  • Colin Fyfe
  • Juan Corchado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2972)


Kernel Maximum Likelihood Hebbian Learning Scale Invariant Maps is a novel technique developed to facilitate the clustering of complex data effectively and efficiently and that is characterised for converging remarkably quickly. The combination of Maximum Likelihood Hebbian Learning Scale Invariant Map and the Kernel Space provides a very smooth scale invariant quantisation which can be used as a clustering technique. The efficiency of this method have been used to analyse an oceanographic problem.


Scale Invariant Output Neuron Kernel Method Kernel Space Winning Neuron 
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

  • Donald MacDonald
    • 1
  • Jos Koetsier
    • 1
  • Emilio Corchado
    • 1
  • Colin Fyfe
    • 1
  • Juan Corchado
    • 2
  1. 1.School of Information and Communication TechnologiesThe University of PaisleyPaisleyScotland
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSpain

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