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Volumn , Issue , 2002, Pages 335-342

Maximum Likelihood and the Information Bottleneck

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EID: 85156256154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (13)

References (11)
  • 5
    • 0032594809 scopus 로고    scopus 로고
    • Histogram clustering for unsupervised segmentation and image retrieval
    • J. Puzicha, T. Hofmann, and J. M. Buhmann. Histogram clustering for unsupervised segmentation and image retrieval. In Pattern Recognition Letters 20(9), 899-909, 1999.
    • (1999) Pattern Recognition Letters , vol.20 , Issue.9 , pp. 899-909
    • Puzicha, J.1    Hofmann, T.2    Buhmann, J. M.3
  • 7
    • 0002788893 scopus 로고    scopus 로고
    • A view of the EM algorithm that justifies incremental, sparse, and other variants
    • M. I. Jordan (editor)
    • R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. In M. I. Jordan (editor), Learning in Graphical Models, pp. 355-368, 1998.
    • (1998) Learning in Graphical Models , pp. 355-368
    • Neal, R. M.1    Hinton, G. E.2
  • 10
    • 0036993190 scopus 로고    scopus 로고
    • Unsupervised document classification using sequential information maximization
    • N. Slonim, N. Friedman, and N. Tishby. Unsupervised document classification using sequential information maximization. In Proc. of SIGIR-25, 2002.
    • (2002) Proc. of SIGIR-25
    • Slonim, N.1    Friedman, N.2    Tishby, N.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.