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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
-
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
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Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006)
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- Date
- 22. 7.2006 16:24:52
-
Wu, M.; Liu, Y.-H.; Brownlee, R.; Zhang, X.: Evaluating utility and automatic classification of subject metadata from Research Data Australia (2021)
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-
Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989)
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- Pages
- S.1-22
- Source
- Klassifikation und Ordnung. Tagungsband 12. Jahrestagung der Gesellschaft für Klassifikation, Darmstadt 17.-19.3.1988. Hrsg.: R. Wille
-
Liu, R.-L.: Context recognition for hierarchical text classification (2009)
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- Date
- 22. 3.2009 19:11:54
-
Yu, W.; Gong, Y.: Document clustering by concept factorization (2004)
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-
Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Date
- 28.10.2013 19:22:57
-
Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004)
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-
Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003)
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-
Chung, Y.M.; Lee, J.Y.: ¬A corpus-based approach to comparative evaluation of statistical term association measures (2001)
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- Abstract
- Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked term pairs and term clusters, analyses of the correlation among the association measures using Pearson's correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule's coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as X**2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the X**2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule's Y seem to overestimate rare terms
-
Huang, Y.-L.: ¬A theoretic and empirical research of cluster indexing for Mandarine Chinese full text document (1998)
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Yang, Y.; Liu, X.: ¬A re-examination of text categorization methods (1999)
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-
Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001)
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-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
-
Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004)
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Yoon, Y.; Lee, G.G.: Efficient implementation of associative classifiers for document classification (2007)
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Ko, Y.; Seo, J.: Text classification from unlabeled documents with bootstrapping and feature projection techniques (2009)
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Xu, Y.; Bernard, A.: Knowledge organization through statistical computation : a new approach (2009)
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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