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Volumn 103, Issue 4, 2007, Pages 131-135

Discriminative learning can succeed where generative learning fails

Author keywords

Algorithms; Computational learning theory; Discriminative learning; Generative learning; Machine learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION USE; LEARNING SYSTEMS; PROBLEM SOLVING;

EID: 34248560655     PISSN: 00200190     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipl.2007.03.004     Document Type: Article
Times cited : (4)

References (12)
  • 4
    • 33646502978 scopus 로고    scopus 로고
    • P. Goldberg, When can two unsupervised learners achieve PAC separation?, in: Proceedings of the 14th Annual COLT, 2001, pp. 303-319
  • 6
    • 84898982939 scopus 로고    scopus 로고
    • Exploiting generative models in discriminative classifiers
    • Morgan Kaufmann
    • Jaakkola T., and Haussler D. Exploiting generative models in discriminative classifiers. Advances in NIPS vol. 11 (1998), Morgan Kaufmann 487-493
    • (1998) Advances in NIPS , vol.11 , pp. 487-493
    • Jaakkola, T.1    Haussler, D.2
  • 8
    • 33746093282 scopus 로고    scopus 로고
    • P. M. Long, R. A. Servedio, Discriminative learning can succeed where generative learning fails, in: Proc. 19th Conference on Computational Learning Theory, 2006, pp. 319-334
  • 9
    • 34248555444 scopus 로고    scopus 로고
    • A. Y. Ng, M. I. Jordan, On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes, NIPS, 2001
  • 10
    • 84898946653 scopus 로고    scopus 로고
    • R. Raina, Y. Shen, A. Y. Ng, A. McCallum, Classification with hybrid generative/discriminative models, NIPS, 2004


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.