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Multiple self-organizing maps for supervised learning

  • Enrique Cervera
  • Angel P. del Pobil
Organization Principles
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)

Abstract

A scheme for supervised learning based on multiple self-organizing maps is presented and its performance is compared with other methods in several pattern classification benchmarks using both synthetic and real data. The advantage of this approach is that the learning method is simplified because the problem is divided into several SOMs, which are trained in the standard unsupervised way. The resulting network preserves the SOM properties like dimensionality reduction and cluster formation, while classifying with an accuaracy comparable to other supervised methods on a wide range of problems.

Keywords

Supervise Learning Supervise Method Sonar Signal Steady State Vowel Vowel Recognition 
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 1995

Authors and Affiliations

  • Enrique Cervera
    • 1
  • Angel P. del Pobil
    • 1
  1. 1.Computer Science Dept.Jaume I UniversityCastellóSpain

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