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Volumn , Issue , 2005, Pages 641-645

Model-based clustering with probabilistic constraints

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER BOUNDARIES; EM-TYPE ALGORITHMS; LOG LIKELIHOOD; MODEL-BASED CLUSTERING; NEW APPROACHES; OBJECTIVE FUNCTIONS; PROBABILISTIC CONSTRAINTS; VARIATIONAL METHODS;

EID: 84880128402     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972757.77     Document Type: Conference Paper
Times cited : (58)

References (20)
  • 8
  • 9
    • 84898995489 scopus 로고    scopus 로고
    • Maximum conditional likelihood via bound maximization and the CEM algorithm
    • MIT Press
    • T. Jebara and A. Pentland. Maximum conditional likelihood via bound maximization and the CEM algorithm. In Advances in Neural Information Processing Systems 11, pages 494-500. MIT Press, 1998.
    • (1998) Advances in Neural Information Processing Systems , vol.11 , pp. 494-500
    • Jebara, T.1    Pentland, A.2
  • 12
    • 9444294778 scopus 로고    scopus 로고
    • From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering
    • D. Klein, S. D. Kamvar, and C. D. Manning. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering. In Proc. Intl. Conf. on Machine Learning, pages 307-314, 2002.
    • (2002) Proc. Intl. Conf. on Machine Learning , pp. 307-314
    • Klein, D.1    Kamvar, S.D.2    Manning, C.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.