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Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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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
-
Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999)
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Braun, T.: Dokumentklassifikation durch Clustering (o.J.)
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Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
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Liu, R.-L.: Context-based term frequency assessment for text classification (2010)
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- Abstract
- Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.
-
Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998)
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Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000)
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Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003)
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-
Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003)
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Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006)
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- Source
- Effektive Information Retrieval Verfahren in Theorie und Praxis: ausgewählte und erweiterte Beiträge des Vierten Hildesheimer Evaluierungs- und Retrievalworkshop (HIER 2005), Hildesheim, 20.7.2005. Hrsg.: T. Mandl u. C. Womser-Hacker
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Jersek, T.: Automatische DDC-Klassifizierung mit Lingo : Vorgehensweise und Ergebnisse (2012)
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Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009)
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- Date
- 22. 8.2009 12:54:24
-
HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016)
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- Date
- 1. 2.2016 18:25:22
-
Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994)
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-
Koch, T.; Vizine-Goetz, D.: Automatic classification and content navigation support for Web services : DESIRE II cooperates with OCLC (1998)
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Koch, T.; Vizine-Goetz, D.: DDC and knowledge organization in the digital library : Research and development. Demonstration pages (1999)
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Koch, T.; Ardö, A.; Noodén, L.: ¬The construction of a robot-generated subject index : DESIRE II D3.6a, Working Paper 1 (1999)
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Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007)
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Leroy, G.; Miller, T.; Rosemblat, G.; Browne, A.: ¬A balanced approach to health information evaluation : a vocabulary-based naïve Bayes classifier and readability formulas (2008)
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Cosh, K.J.; Burns, R.; Daniel, T.: Content clouds : classifying content in Web 2.0 (2008)
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