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Boosting Unsupervised Competitive Learning Ensembles

  • Emilio Corchado
  • Bruno Baruque
  • Hujun Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)

Abstract

Topology preserving mappings are great tools for data visualization and inspection in large datasets. This research presents a combination of several topology preserving mapping models with some basic classifier ensemble and boosting techniques in order to increase the stability conditions and, as an extension, the classification capabilities of the former. A study and comparison of the performance of some novel and classical ensemble techniques are presented in this paper to test their suitability, both in the fields of data visualization and classification when combined with topology preserving models such as the SOM, ViSOM or ML-SIM.

Keywords

topology preserving mappings boosting bagging unsupervised learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Emilio Corchado
    • 1
  • Bruno Baruque
    • 1
  • Hujun Yin
    • 2
  1. 1.Department of Civil Engineering. University of BurgosSpain
  2. 2.School of Electrical and Electronic Engineering. University of ManchesterUK

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