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Relevance and Kernel Self-Organising Maps

  • Emilio Corchado
  • Colin Fyfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2714)

Abstract

We review the recently proposed method of Kernel Self-organising maps (KSOM) which has been shown to exhibit very fast convergence. We show that this is due to an interaction between the fact that we are working in an overcomplete basis and the fact that we are using a mixture of one-shot and incremental learning. We then review Relevance Vector Machines which is a supervised training method related to Support Vector Machines and apply it to creating the Relevance Self-Organising Map, RSOM. We show results on artificial data and on the iris data set.

Keywords

Sparse Representation Neighbourhood Function Relevance Vector Machine 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 2003

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Colin Fyfe
    • 1
  1. 1.School of Information and Communication TechnologiesThe University of PaisleyScotland

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