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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/3904

タイトル: Pattern Recognition in Metabonomics Using Self Organizing Maps
著者: Stefan, W. Roeder
Ulrike, Rolle-Kampczyk
Olf, Herbarth
キーワード: stress patterns
pattern recognition
artificial neural networks
self organizing maps
metabonomics
発行日: Nov-2005
出版者: JAIST Press
抄録: The contiguity between external exposure and internal stress burden in humans is linked by metabolic pathways. To gather deeper knowledge about these links it is necessary to look behind the scenes and to find out which variables affect each other as well as which groups of individuals can be described. The approach shown in this paper is based on the algorithm of self organizing maps (SOM), which have shown their capabilities for clustering tasks previously. They enable us to subdivide cases into classes without prior knowledge. Furthermore prediction of selected outcome variables is possible after training of the algorithm. New visualization techniques atop of the SOM algorithm allow a multidimensional view on the data and their immanent structures as well as on the classes found. Finally we checked several parameter combinations of the SOM for their applicability and performance with our data.
記述: The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.html
IFSR 2005 : Proceedings of the First World Congress of the International Federation for Systems Research : The New Roles of Systems Sciences For a Knowledge-based Society : Nov. 14-17, 2114, Kobe, Japan
Symposium 5, Session 1 : Data/Text Mining from Large Databases Data Mining
言語: ENG
URI: http://hdl.handle.net/10119/3904
ISBN: 4-903092-02-X
出現コレクション:IFSR 2005

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