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Volumn , Issue , 2006, Pages 1417-1424

Generalized Maximum Margin Clustering and Unsupervised Kernel Learning

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; LARGE DATASET; SUPPORT VECTOR MACHINES;

EID: 85075958051     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (19)

References (15)
  • 3
    • 0001138328 scopus 로고
    • A k-means clustering algorithm
    • J. Hartigan and M. Wong. A k-means clustering algorithm. Appl. Statist., 28:100-108, 1979.
    • (1979) Appl. Statist , vol.28 , pp. 100-108
    • Hartigan, J.1    Wong, M.2
  • 4
    • 0021404166 scopus 로고
    • Mixture densities, maximum likelihood and the em algorithm
    • R. A. Redner and H. F. Walker. Mixture densities, maximum likelihood and the em algorithm. SIAM Review, 26:195-239, 1984.
    • (1984) SIAM Review , vol.26 , pp. 195-239
    • Redner, R. A.1    Walker, H. F.2
  • 14
    • 0242320492 scopus 로고    scopus 로고
    • Uniqueness theorems for kernel methods
    • C. J. C. Burges and D. J. Crisp. Uniqueness theorems for kernel methods. Neurocomputing, 55(1-2):187-220, 2003.
    • (2003) Neurocomputing , vol.55 , Issue.1-2 , pp. 187-220
    • Burges, C. J. C.1    Crisp, D. J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.