Self-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Genetics

dc.contributor.authorGuimarães, Gabrielade
dc.contributor.authorUrfer, Wolfgangde
dc.date.accessioned2004-12-06T18:42:24Z
dc.date.available2004-12-06T18:42:24Z
dc.date.issued2000de
dc.description.abstractThis 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.en
dc.format.extent1599724 bytes
dc.format.extent253810 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5028
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-5478
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subjectHidden Markov Modelsen
dc.subjectprotein sequencesen
dc.subjectsleep apneaen
dc.subjecttumor classificationen
dc.subjectunsupervised neural networksen
dc.subject.ddc310de
dc.titleSelf-Organizing Maps and its Applications in Sleep Apnea Research and Molecular Geneticsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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