Search (32 results, page 1 of 2)

  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[1990 TO 2000}
  1. Wätjen, H.-J.; Diekmann, B.; Möller, G.; Carstensen, K.-U.: Bericht zum DFG-Projekt: GERHARD : German Harvest Automated Retrieval and Directory (1998) 0.03 
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  2. Yang, Y.; Liu, X.: ¬A re-examination of text categorization methods (1999) 0.01 
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    Abstract
    This paper reports a controlled study with statistical significance tests an five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a Naive Bayes (NB) classifier. We focus an the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category are small (less than ten, and that all the methods perform comparably when the categories are sufficiently common (over 300 instances).
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  3. Savic, D.: Designing an expert system for classifying office documents (1994) 0.01 
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    Language
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    Source
    Records management quarterly. 28(1994) no.3, S.20-29
  4. Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995) 0.01 
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    Language
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    Source
    Records management quarterly. 29(1995) no.4, S.3-18
  5. Ruocco, A.S.; Frieder, O.: Clustering and classification of large document bases in a parallel environment (1997) 0.01 
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    Date
    29. 7.1998 17:45:02
    Language
    e
  6. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
    Language
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  7. 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
    Language
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  8. May, A.D.: Automatic classification of e-mail messages by message type (1997) 0.00 
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    Abstract
    This article describes a system that automatically classifies e-mail messages in the HUMANIST electronic discussion group into one of 4 classes: questions, responses, announcement or administartive. A total of 1.372 messages were analyzed. The automatic classification of a message was based on string matching between a message text and predefined string sets for each of the massage types. The system's automated ability to accurately classify a message was compared against manually assigned codes. The Cohen's Kappa of .55 suggested that there was a statistical agreement between the automatic and manually assigned codes
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  9. Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999) 0.00 
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  10. McKiernan, G.: Automated categorisation of Web resources : a profile of selected projects, research, products, and services (1996) 0.00 
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  11. Vizine-Goetz, D.: NetLab / OCLC collaboration seeks to improve Web searching (1999) 0.00 
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  12. Möller, G.: Automatic classification of the World Wide Web using Universal Decimal Classification (1999) 0.00 
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  13. Subramanian, S.; Shafer, K.E.: Clustering (1998) 0.00 
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  14. Shafer, K.E.: Evaluating Scorpion results (1998) 0.00 
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  15. Cheng, P.T.K.; Wu, A.K.W.: ACS: an automatic classification system (1995) 0.00 
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  16. Losee, R.M.; Haas, S.W.: Sublanguage terms : dictionaries, usage, and automatic classification (1995) 0.00 
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  17. Ingwersen, P.; Wormell, I.: Ranganathan in the perspective of advanced information retrieval (1992) 0.00 
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  18. Chan, L.M.; Lin, X.; Zeng, M.: Structural and multilingual approaches to subject access on the Web (1999) 0.00 
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  19. Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994) 0.00 
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  20. Losee, R.M.: Text windows and phrases differing by discipline, location in document, and syntactic structure (1996) 0.00 
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