Search (33 results, page 1 of 2)

Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.07

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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

Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.03

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Date: 12. 9.2004 9:56:22
Year: 2003

Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.03

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Source: c't. 2003, H.19, S.192-197
Year: 2003

Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.03

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Source: Ariadne magazine. 2003, no.37
Year: 2003

Miyamoto, S.: Information clustering based an fuzzy multisets (2003) 0.03

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Source: Information processing and management. 39(2003) no.2, S.195-213
Year: 2003

Drori, O.; Alon, N.: Using document classification for displaying search results (2003) 0.02

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Source: Journal of information science. 29(2003) no.2, S.97-106
Year: 2003

Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.02

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Source: Journal of information science. 29(2003) no.2, S.117-126
Year: 2003

Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003) 0.02

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Source: Journal of the American Society for Information Science and technology. 54(2003) no.10, S.966-975
Year: 2003

Sun, A.; Lim, E.-P.; Ng, W.-K.: Performance measurement framework for hierarchical text classification (2003) 0.02

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Source: Journal of the American Society for Information Science and technology. 54(2003) no.11, S.1014-1028
Year: 2003

Godby, C.J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization : subject access issues (2003) 0.02

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Year: 2003

Zhang, X: Rough set theory based automatic text categorization (2005) 0.02

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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.

Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003) 0.02

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Source: Journal of the American Society for Information Science and technology. 54(2003) no.14, S.1269-1277
Year: 2003

Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003) 0.02

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Source: Information processing and management. 39(2003) no.1, S.25-44
Year: 2003

Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02

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Date: 5. 5.2003 14:17:22

Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02

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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) 0.02

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Date: 1. 2.2016 18:25:22

Adams, K.C.: Word wranglers : Automatic classification tools transform enterprise documents from "bags of words" into knowledge resources (2003) 0.01

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Year: 2003

Zhou, G.D.; Zhang, M.; Ji, D.H.; Zhu, Q.M.: Hierarchical learning strategy in semantic relation extraction (2008) 0.01
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Abstract

This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and guide the discriminative function learning in the lower-level one more effectively, which otherwise might suffer from limited training data. In this paper, two classifier learning approaches, i.e. the simple perceptron algorithm and the state-of-the-art Support Vector Machines, are applied using the hierarchical learning strategy. Moreover, several kinds of class hierarchies either manually predefined or automatically clustered are explored and compared. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.
Reiner, U.: VZG-Projekt Colibri : Bewertung von automatisch DDC-klassifizierten Titeldatensätzen der Deutschen Nationalbibliothek (DNB) (2009) 0.01
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Abstract

Das VZG-Projekt Colibri/DDC beschäftigt sich seit 2003 mit automatischen Verfahren zur Dewey-Dezimalklassifikation (Dewey Decimal Classification, kurz DDC). Ziel des Projektes ist eine einheitliche DDC-Erschließung von bibliografischen Titeldatensätzen und eine Unterstützung der DDC-Expert(inn)en und DDC-Laien, z. B. bei der Analyse und Synthese von DDC-Notationen und deren Qualitätskontrolle und der DDC-basierten Suche. Der vorliegende Bericht konzentriert sich auf die erste größere automatische DDC-Klassifizierung und erste automatische und intellektuelle Bewertung mit der Klassifizierungskomponente vc_dcl1. Grundlage hierfür waren die von der Deutschen Nationabibliothek (DNB) im November 2007 zur Verfügung gestellten 25.653 Titeldatensätze (12 Wochen-/Monatslieferungen) der Deutschen Nationalbibliografie der Reihen A, B und H. Nach Erläuterung der automatischen DDC-Klassifizierung und automatischen Bewertung in Kapitel 2 wird in Kapitel 3 auf den DNB-Bericht "Colibri_Auswertung_DDC_Endbericht_Sommer_2008" eingegangen. Es werden Sachverhalte geklärt und Fragen gestellt, deren Antworten die Weichen für den Verlauf der weiteren Klassifizierungstests stellen werden. Über das Kapitel 3 hinaus führende weitergehende Betrachtungen und Gedanken zur Fortführung der automatischen DDC-Klassifizierung werden in Kapitel 4 angestellt. Der Bericht dient dem vertieften Verständnis für die automatischen Verfahren.

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

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Pages: S.1-22

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