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Volumn 1, Issue , 2010, Pages 3-6

Web service classification using support vector machine

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION METHODS; CONVENTIONAL CLASSIFICATION METHODS; DESCRIPTIVE INFORMATION; EXISTING METHOD; FEATURE SELECTION METHODS; NOVEL METHODS; REAL WORLD SETTING; SAMPLE COLLECTION; SAMPLE DATA; SERVICE DISCOVERY;

EID: 78751533734     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2010.9     Document Type: Conference Paper
Times cited : (74)

References (10)
  • 2
    • 67651253193 scopus 로고    scopus 로고
    • On the combination of textual and semantic descriptions for automated semantic web service classification
    • AIAI, Springer
    • Ioannis Katakis, Georgios Meditskos, Grigorios Tsoumakas, Nick Bassiliades, and Ioannis P. Vlahavas. On the combination of textual and semantic descriptions for automated semantic web service classification. In AIAI, volume 296 of IFIP, pages 95-104. Springer, 2009.
    • (2009) IFIP , vol.296 , pp. 95-104
    • Katakis, I.1    Meditskos, G.2    Tsoumakas, G.3    Bassiliades, N.4    Vlahavas, I.P.5
  • 4
    • 84957069814 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • Springer
    • Thorsten Joachims. Text categorization with support vector machines: Learning with many relevant features. In Machine Learning: ECML-98, pages 137-142. Springer, 1998.
    • (1998) Machine Learning: ECML-98 , pp. 137-142
    • Joachims, T.1
  • 5
    • 33846979476 scopus 로고    scopus 로고
    • Classifying web documents in a hierarchy of categories: A comprehensive study
    • Michelangelo Ceci and Donato Malerba. Classifying web documents in a hierarchy of categories: a comprehensive study. J. Intell. Inf. Syst., 28(1):37-78, 2007.
    • (2007) J. Intell. Inf. Syst. , vol.28 , Issue.1 , pp. 37-78
    • Ceci, M.1    Malerba, D.2
  • 6
    • 70449708973 scopus 로고    scopus 로고
    • Optimal feature selection for support vector machines
    • Minh Hoai Nguyen and Fernando de la Torre. Optimal feature selection for support vector machines. Pattern Recogn., 43(3):584-591, 2010.
    • (2010) Pattern Recogn. , vol.43 , Issue.3 , pp. 584-591
    • Nguyen, M.H.1    De La Torre, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.