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Self-organizing maps and its applications in sleep apnea research and molecular genetics

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  • Guimaraes, Gabriela
  • Urfer, Wolfgang

Abstract

This paper presents the application of special unsupervised neural networks (self-organizing maps) to different domains, as sleep apnea discovery, protein sequences analysis and tumor classification. An enhancement of the original algorithm, as well as the introduction of several hierachical levels enables the discovery of complex structures as present in this type of applications. Furthermore, an integration of unsupervised neural networks with hidden markov models is proposed.

Suggested Citation

  • Guimaraes, Gabriela & Urfer, Wolfgang, 2000. "Self-organizing maps and its applications in sleep apnea research and molecular genetics," Technical Reports 2000,23, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200023
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    File URL: https://www.econstor.eu/bitstream/10419/77307/2/2000-23.pdf
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    References listed on IDEAS

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    1. Brunnert, Marcus & Müller, Oliver & Urfer, Wolfgang, 2000. "Genetical and statistical aspects of polymerase chain reactions," Technical Reports 2000,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Zerbst, Matthias & Tschiersch, Lars & Talbi, Mohamed & Guimarães, Gabriela & Urfer, Wolfgang, 2000. "Clustering algorithms for aerial photographs and high resolution satellite images," Technical Reports 2000,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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