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Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.24

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Content: Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
Date: 8. 1.2013 10:22:32
Source: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK

Frobese, D.T.: Klassifikationsaufgaben mit der SENTRAX : Konkreter Fall: Automatische Detektion von SPAM (2006) 0.00

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Abstract: Die Suchfunktionen des SENTRAX-Verfahrens werden für die Klassifizierung von Mails und im Besonderen für die Detektion von SPAM eingesetzt. Die Eigenschaften einer kontextähnlichen Suche und die Fehlertoleranz sollen genutzt werden, um SPAM Nachrichten treffsicher aufzuspüren.
Footnote: Beitrag der Proceedings des Fünften Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2006), Hildesheim, xx.x.2006.

Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00

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Date: 11. 5.2003 18:29:44

Zhang, X: Rough set theory based automatic text categorization (2005) 0.00

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Abstract: Der Forschungsbericht "Rough Set Theory Based Automatic Text Categorization and the Handling of Semantic Heterogeneity" von Xueying Zhang ist in Buchform auf Englisch erschienen. Zhang hat in ihrer Arbeit ein Verfahren basierend auf der Rough Set Theory entwickelt, das Beziehungen zwischen Schlagwörtern verschiedener Vokabulare herstellt. Sie war von 2003 bis 2005 Mitarbeiterin des IZ und ist seit Oktober 2005 Associate Professor an der Nanjing University of Science and Technology.

Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.00

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Source: Knowledge organization. 29(2002) nos.3/4, S.181-197

Sebastiani, F.: Machine learning in automated text categorization (2002) 0.00

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Source: ACM computing surveys. 34(2002) no.1, S.1-47

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