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Volumn 36, Issue 2, 1988, Pages 177-221

Quantifying inductive bias: AI learning algorithms and Valiant's learning framework

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING - ALGORITHMS; SYSTEMS SCIENCE AND CYBERNETICS - LEARNING SYSTEMS;

EID: 0024082469     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/0004-3702(88)90002-1     Document Type: Article
Times cited : (318)

References (46)
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  • 5
    • 84911653375 scopus 로고    scopus 로고
    • Blumer, A., Ehrenfeucht, A., Haussler, D. and Warmuth, M., Learnability and the Vapnik-Chervonenkis dimension, J. ACM, to appear.
  • 12
    • 84911593690 scopus 로고    scopus 로고
    • Ehrenfeucht, A., Haussler, D., Kearns, M. and Valiant, L.G., A general lower bound on the number of examples needed for learning, Inf. Comput., to appear.
  • 15
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    • Haussler, D., Learning conjunctive concepts in structural domains, Machine Learning, to appear.
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  • 24
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    • Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
    • (1988) Machine Learning , vol.2 , Issue.4 , pp. 245-318
    • Littlestone1
  • 27
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    • The need for biases in learning generalizations
    • Department of Computer Science, Rutgers University, New Brunswick, NJ
    • (1980) Tech. Rept. CBM-TR-117
    • Mitchell1
  • 31
    • 0018058024 scopus 로고
    • On the connection between the complexity and credibility of inferred models
    • (1978) Int. J. General Syst. , vol.4 , pp. 255-264
    • Pearl1
  • 33
    • 0001594860 scopus 로고
    • A general framework for induction and a study of selective induction
    • (1986) Machine Learning , vol.1 , Issue.2 , pp. 177-226
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    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • (1978) Automatica , vol.14 , pp. 465-471
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  • 37
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    • Learning concepts by asking questions
    • R. Michalski, J. Carbonell, T. Mitchell, Morgan Kaufmann, Los Altos, CA
    • (1986) Machine Learning , vol.2
    • Sammut1    Banerji2
  • 39
    • 0001164493 scopus 로고
    • Shift of bias for inductive concept learning
    • R. Michalski, J. Carbonell, T. Mitchell, Morgan Kaufmann, Los Altos, CA
    • (1986) Machine Learning , vol.2
    • Utgoff1
  • 40
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    • A theory of the learnable
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant1
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    • Computational limitations on learning from examples
    • Aiken Computing Lab., Harvard University, Cambridge, MA, also: J. ACM, to appear
    • (1986) Tech. Rept. TR-05-86
    • Pitt1    Valiant2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.