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

Gabriela Guimaraes and Wolfgang Urfer

No 2000,23, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

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.

Keywords: Unsupervised Neural Networks; Hidden Markov Models; Sleep Apnea; Protein Sequences; Tumor Classification (search for similar items in EconPapers)
Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200023

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