Multiple topic detection by Parametric Mixture Models (PMM) - Automatic web page categorization for browsing

NTT Technical Review

Volumn 3, Issue 3, 2005, Pages 15-18

  Saito, Kazumi ^a  

^a Nippon Telegraph and Telephone Corporation   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONTENT BASED RETRIEVAL; FREQUENCIES; HIERARCHICAL SYSTEMS; INFORMATION ANALYSIS; LEARNING ALGORITHMS;

MULTI TOPIC DETECTION MODEL; NAIVE BAYES (NB); PARAMETRIC MIXTURE MODELS (PMM); SUPPORT VECTOR MACHINES (SVM);

WORLD WIDE WEB;

EID: 18944373289     PISSN: 13483447     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (5)

1. http://dmoz.org/
2. N. Ueda and K. Saito, "Parametric mixture models for multiple-topic text," Trans. of IEICE, Vol. J87-D-II, No. 3, pp. 872-883, 2004.
3. N. Ueda and K. Saito, "Single-shot Detection of Multi-category of Text using Parametric Mixture Models," Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD2002), Edmonton, Alberta, Canada, pp. 626-631, 2002.
4. N. Ueda and K. Saito, "Multiple-topic model for topic-based text retrieval," Trans. of IPSJ, Vol. 44, No. SIG14 (TOM 9), pp. 1-8, 2003.
5. T. Yamada, "Visualizing Large-scale Network Data Structures," NTT Technical Review, Vol. 3, No. 3, pp. 19-23, 2005.