Search (178 results, page 3 of 9)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Shafer, K.E.: Evaluating Scorpion Results (2001) 0.00 
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  2. Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004) 0.00 
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  3. Sebastiani, F.: Classification of text, automatic (2006) 0.00 
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    Abstract
    Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to e-mail routing, spam filtering, authorship attribution, and automated survey coding. This article will focus on the ML approach to ATC, whereby a software system (called the learner) automatically builds a classifier for the categories of interest by generalizing from a "training" set of pre-classified texts.
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  4. Sun, A.; Lim, E.-P.; Ng, W.-K.: Performance measurement framework for hierarchical text classification (2003) 0.00 
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  5. Frank, E.; Paynter, G.W.: Predicting Library of Congress Classifications from Library of Congress Subject Headings (2004) 0.00 
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  6. Díaz, I.; Ranilla, J.; Montañes, E.; Fernández, J.; Combarro, E.F.: Improving performance of text categorization by combining filtering and support vector machines (2004) 0.00 
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  7. Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00 
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    Abstract
    Topic discovery is an important means for marketing, e-Business and social science studies. As well, it can be applied to various purposes, such as identifying a group with certain properties and observing the emergence and diminishment of a certain cyber community. Previous topic discovery work (J.M. Kleinberg, Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, California, p. 668) requires manual judgment of usefulness of outcomes and is thus incapable of handling the explosive growth of the Internet. In this paper, we propose the Automatic Topic Discovery (ATD) method, which combines a method of base set construction, a clustering algorithm and an iterative principal eigenvector computation method to discover the topics relevant to a given query without using manual examination. Given a query, ATD returns with topics associated with the query and top representative pages for each topic. Our experiments show that the ATD method performs better than the traditional eigenvector method in terms of computation time and topic discovery quality.
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  8. Aphinyanaphongs, Y.; Fu, L.D.; Li, Z.; Peskin, E.R.; Efstathiadis, E.; Aliferis, C.F.; Statnikov, A.: ¬A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization (2014) 0.00 
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  9. Barbu, E.: What kind of knowledge is in Wikipedia? : unsupervised extraction of properties for similar concepts (2014) 0.00 
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  10. Savic, D.: Designing an expert system for classifying office documents (1994) 0.00 
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  11. Bollmann, P.; Konrad, E.; Schneider, H.-J.; Zuse, H.: Anwendung automatischer Klassifikationsverfahren mit dem System FAKYR (1978) 0.00 
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  12. Cheng, P.T.K.; Wu, A.K.W.: ACS: an automatic classification system (1995) 0.00 
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  13. Losee, R.M.; Haas, S.W.: Sublanguage terms : dictionaries, usage, and automatic classification (1995) 0.00 
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  14. Ingwersen, P.; Wormell, I.: Ranganathan in the perspective of advanced information retrieval (1992) 0.00 
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  15. Chan, L.M.; Lin, X.; Zeng, M.: Structural and multilingual approaches to subject access on the Web (1999) 0.00 
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  16. Godby, C. J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization (2001) 0.00 
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  17. Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000) 0.00 
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  18. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.00 
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    Abstract
    Rechnung, Kündigung oder Adressänderung? Eingehende Briefe und E-Mails werden immer häufiger von Software statt aufwändig von Menschenhand sortiert. Die Textklassifizierer arbeiten erstaunlich genau. Sie fahnden auch nach ähnlichen Texten und sorgen so für einen schnellen Überblick. Ihre Werkzeuge sind Linguistik, Statistik und Logik
  19. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.00 
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  20. Zhang, X: Rough set theory based automatic text categorization (2005) 0.00 
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