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Volumn 1316, Issue , 1997, Pages 85-99

Partial occam’s razor and its applications

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; POLYNOMIAL APPROXIMATION;

EID: 84958035056     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-63577-7_37     Document Type: Conference Paper
Times cited : (1)

References (22)
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    • (1994) Artificial Intelligence , vol.69 , Issue.1-2 , pp. 279-306
    • Almuallim, H.1    Dietterich, T.G.2
  • 3
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    • Learning from noisy examples
    • D. Angluin and P.D. Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.
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    • Angluin, D.1    Laird, P.D.2
  • 5
    • 0026883136 scopus 로고
    • On the necessity of Occam algorithms
    • R. Bard and L. Pitt. On the necessity of Occam algorithms. Theoretical Computer Science, 100:157-184, 1992.
    • (1992) Theoretical Computer Science , vol.100 , pp. 157-184
    • Bard, R.1    Pitt, L.2
  • 7
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121(2):256-285, 1995.
    • (1995) Information and Computation , vol.121 , Issue.2 , pp. 256-285
    • Freund, Y.1
  • 8
    • 0006494007 scopus 로고
    • An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
    • IEEE Computer Society Press~ Los Alamitos, CA
    • Jeffrey Jackson. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. In Proceedings of the 35rd Annual Symposium on Foundations of Computer Science, pages 42-53. IEEE Computer Society Press~ Los Alamitos, CA, 1994.
    • (1994) Proceedings of the 35Rd Annual Symposium on Foundations of Computer Science , pp. 42-53
    • Jackson, J.1
  • 10
    • 0024082469 scopus 로고
    • Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
    • D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence, 36:177-221, 1988.
    • (1988) Artificial Intelligence , vol.36 , pp. 177-221
    • Haussler, D.1
  • 16
    • 34250091945 scopus 로고
    • Learning when irrelevant attributes abound: A new linear-threshold algorithm
    • N. Littlestone. Learning when irrelevant attributes abound: A new linear-threshold algorithm. Machine Learning, 2:285-318, 1988.
    • (1988) Machine Learning , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 18
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    • Learning decision lists
    • R.L. Rivest. Learning decision lists. Machine Learning, 2(3):229-246, 1987.
    • (1987) Machine Learning , vol.2 , Issue.3 , pp. 229-246
    • Rivest, R.L.1
  • 19
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • R.E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197-227, 1990.
    • (1990) Machine Learning , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.E.1
  • 20
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L.G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
    • (1984) Communications of the ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.