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Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.31

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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

Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.01

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Date: 5. 5.2003 14:17:22

Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01

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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) 0.01

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Date: 1. 2.2016 18:25:22

Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005) 0.01
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Footnote

Zum Inhalt Auf einen kurzen einleitenden Abschnitt folgt eine Einführung in die grundlegende Methodik des automatischen Klassifizierens. Oberhauser erklärt hier Begriffe wie Einfach- und Mehrfachklassifizierung, Klassen- und Dokumentzentrierung, und geht danach auf die hauptsächlichen Anwendungen der automatischen Klassifikation von Textdokumenten, maschinelle Lernverfahren und Techniken der Dimensionsreduktion bei der Indexierung ein. Zwei weitere Unterkapitel sind der Erstellung von Klassifikatoren und den Methoden für deren Auswertung gewidmet. Das Kapitel wird abgerundet von einer kurzen Auflistung einiger Softwareprodukte für automatisches Klassifizieren, die sowohl kommerzielle Software, als auch Projekte aus dem Open-Source-Bereich umfasst. Der Hauptteil des Buches ist den großen Projekten zur automatischen Erschließung von Webdokumenten gewidmet, die von OCLC (Scorpion) sowie an den Universitäten Lund (Nordic WAIS/WWW, DESIRE II), Wolverhampton (WWLib-TOS, WWLib-TNG, Old ACE, ACE) und Oldenburg (GERHARD, GERHARD II) durchgeführt worden sind. Der Autor beschreibt hier sehr detailliert - wobei der Detailliertheitsgrad unterschiedlich ist, je nachdem, was aus der Projektdokumentation geschlossen werden kann - die jeweilige Zielsetzung des Projektes, die verwendete Klassifikation, die methodische Vorgehensweise sowie die Evaluierungsmethoden und -ergebnisse. Sofern Querverweise zu anderen Projekten bestehen, werden auch diese besprochen. Der Verfasser geht hier sehr genau auf wichtige Aspekte wie Vokabularbildung, Textaufbereitung und Gewichtung ein, so dass der Leser eine gute Vorstellung von den Ansätzen und der möglichen Weiterentwicklung des Projektes bekommt. In einem weiteren Kapitel wird auf einige kleinere Projekte eingegangen, die dem für Bibliotheken besonders interessanten Thema des automatischen Klassifizierens von Büchern sowie den Bereichen Patentliteratur, Mediendokumentation und dem Einsatz bei Informationsdiensten gewidmet sind. Die Darstellung wird ergänzt von einem Literaturverzeichnis mit über 250 Titeln zu den konkreten Projekten sowie einem Abkürzungs- und einem Abbildungsverzeichnis. In der abschließenden Diskussion der beschriebenen Projekte wird einerseits auf die Bedeutung der einzelnen Projekte für den methodischen Fortschritt eingegangen, andererseits aber auch einiges an Kritik geäußert, v. a. bezüglich der mangelnden Auswertung der Projektergebnisse und des Fehlens an brauchbarer Dokumentation. So waren z. B. die Projektseiten des Projekts GERHARD (www.gerhard.de/) auf den Stand von 1998 eingefroren, zurzeit [11.07.06] sind sie überhaupt nicht mehr erreichbar. Mit einigem Erstaunen stellt Oberhauser auch fest, dass - abgesehen von der fast 15 Jahre alten Untersuchung von Larsen - »keine signifikanten Studien oder Anwendungen aus dem Bibliotheksbereich vorliegen« (S. 139). Wie der Autor aber selbst ergänzend ausführt, dürfte dies daran liegen, dass sich bibliografische Metadaten wegen des geringen Textumfangs sehr schlecht für automatische Klassifikation eignen, und dass - wie frühere Ergebnisse gezeigt haben - das übliche TF/IDF-Verfahren nicht für Katalogisate geeignet ist (ibd.).

Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01

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Pages: S.1-22

Dubin, D.: Dimensions and discriminability (1998) 0.01

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Date: 22. 9.1997 19:16:05

Automatic classification research at OCLC (2002) 0.01

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Date: 5. 5.2003 9:22:09

Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.01

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Date: 1. 8.1996 22:08:06

Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01

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Date: 22. 7.2006 16:24:52

Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01

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Date: 22. 9.2008 18:31:54

Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01

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Date: 22. 3.2009 19:11:54

Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01

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Date: 22. 8.2009 19:51:28

Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.01

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Date: 23. 3.2013 13:22:36

Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.01

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Date: 4. 8.2015 19:22:04

Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01

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Date: 22. 3.2009 19:14:43

Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.01

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Date: 28.10.2013 19:22:57

Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.00

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Date: 12. 9.2004 9:56:22

Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.00

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Date: 22. 1.2010 14:41:24

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