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Volumn , Issue , 2000, Pages 914-920

Learning the similarity of documents: An information-geometric approach to document retrieval and categorization

Author keywords

[No Author keywords available]

Indexed keywords

DOCUMENT RETRIEVAL; FISHER KERNELS; GENERAL METHOD; INFORMATION SOURCES; LABELED AND UNLABELED DATA; SIMILARITY FUNCTIONS; SUPERVISED LEARNING PROBLEMS; TEXT CATEGORIZATION;

EID: 84898996741     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (91)

References (15)
  • 3
    • 0000731106 scopus 로고
    • Latent class analysis of two-way contingency tables by Bayesian methods
    • M. J. Evans, Z. Gilula, and I. Guttman. Latent class analysis of two-way contingency tables by Bayesian methods. Biometrika, 76(3):557-563, 1989.
    • (1989) Biometrika , vol.76 , Issue.3 , pp. 557-563
    • Evans, M.J.1    Gilula, Z.2    Guttman, I.3
  • 4
    • 0003754936 scopus 로고    scopus 로고
    • Stratified exponential families: Graphical models and model selection
    • Microsoft Research
    • D. Geiger, D. Heckerman, H. King, and C. Meek. Stratified exponential families: Graphical models and model selection. Technical Report MSR-TR-98-31, Microsoft Research, 1998.
    • (1998) Technical Report MSR-TR-98-31
    • Geiger, D.1    Heckerman, D.2    King, H.3    Meek, C.4
  • 10
    • 0000636553 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In International Conference on Machine Learning (ECML), 1998.
    • (1998) International Conference on Machine Learning (ECML)
    • Joachims, T.1
  • 12
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. Lee and S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999.
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.1    Seung, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.