메뉴 건너뛰기




Volumn , Issue , 1997, Pages 55-63

Mistake-driven learning in text categorization

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 78049288875     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (111)

References (24)
  • 2
    • 0007563290 scopus 로고
    • Learning boolean functions in an infinite attribute space
    • October
    • Blum, A. 1992. Learning boolean functions in an infinite attribute space. Machine Learning, 9(4):373-386, October.
    • (1992) Machine Learning , vol.9 , Issue.4 , pp. 373-386
    • Blum, A.1
  • 3
    • 85152618419 scopus 로고
    • Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain
    • Morgan Kaufmann
    • Blum, A. 1995. Empirical support for Winnow and weighted-majority based algorithms: results on a calendar scheduling domain. In Proc. 12th International Conference on Machine Learning, pages 64-72. Morgan Kaufmann.
    • (1995) Proc. 12th International Conference on Machine Learning , pp. 64-72
    • Blum, A.1
  • 12
    • 84969342575 scopus 로고
    • The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant
    • b pages ACM Press, New York, NY
    • Kivinen, J. and M. K. Warmuth. 1995b. The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. In Proc. 8th Annu. Conf. on Comput. Learning Theory, pages 289-296. ACM Press, New York, NY.
    • (1995) Proc. 8th Annu. Conf. on Comput. Learning Theory , pp. 289-296
    • Kivinen, J.1    Warmuth, M. K.2
  • 16
    • 34250091945 scopus 로고
    • Learning quickly when irrelevant attributes abound: A new finear-threshold algorithm
    • Littlestone, N. 1988. Learning quickly when irrelevant attributes abound: A new finear-threshold algorithm. Machine Learning, 2:285-318.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 17
    • 0000511449 scopus 로고
    • Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow
    • San Mateo, CA. Morgan Kanfmann
    • Littlestone, N. 1991. Redundant noisy attributes, attribute errors, and linear threshold learning using Winnow. In Proc. $th Annu. Workshop on Corn-put. Learning Theory, pages 147-156, San Mateo, CA. Morgan Kanfmann.
    • (1991) Proc. $th Annu. Workshop on Corn-put. Learning Theory , pp. 147-156
    • Littlestone, N.1
  • 18
    • 85050937116 scopus 로고
    • Comparing severallinear-threshold learning algorithms on tasks involving superfluous attributes
    • Morgan Kaufmann
    • Littlestone, N. 1995. Comparing severallinear-threshold learning algorithms on tasks involving superfluous attributes. In Proc. 12th International Conference on Machine Learning, pages 353-361. Morgan Kaufmann.
    • (1995) Proc. 12th International Conference on Machine Learning , pp. 353-361
    • Littlestone, N.1
  • 21
    • 11144273669 scopus 로고
    • The perceptron: A probabilistic model for information storage and organization in the brain
    • (Reprinted in Neurocomputing (MIT Press, 1988))
    • Rosenblatt, F. 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65:386-407. (Reprinted in Neurocomputing (MIT Press, 1988).).
    • (1958) Psychological Review , vol.65 , pp. 386-407
    • Rosenblatt, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.