메뉴 건너뛰기




Volumn 52, Issue 2, 1996, Pages 239-254

General bounds on the number of examples needed for learning probabilistic concepts

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBABILITY; TREES (MATHEMATICS);

EID: 0030128944     PISSN: 00220000     EISSN: None     Source Type: Journal    
DOI: 10.1006/jcss.1996.0019     Document Type: Article
Times cited : (31)

References (17)
  • 1
    • 22944444113 scopus 로고
    • On the computational complexity of approximating distributions by probabilistic automata
    • Kaufmann, Los Angeles, CA
    • N. Abe and M. K. Warmuth, On the computational complexity of approximating distributions by probabilistic automata, in "Proceedings, 3rd Annual Workshop on Computational Learning Theory," pp. 52-66, Kaufmann, Los Angeles, CA, 1990.
    • (1990) Proceedings, 3rd Annual Workshop on Computational Learning Theory , pp. 52-66
    • Abe, N.1    Warmuth, M.K.2
  • 3
    • 0000492326 scopus 로고
    • Learning from noisy examples
    • D. Angluin and P. Laird, Learning from noisy examples, Mach. Learning 2, No. 4 (1988), 343-370.
    • (1988) Mach. Learning , vol.2 , Issue.4 , pp. 343-370
    • Angluin, D.1    Laird, P.2
  • 7
    • 0024739191 scopus 로고
    • A general lower bound on the number of examples needed for learning
    • A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant, A general lower bound on the number of examples needed for learning, Inform. and Comput. 82, No. 3 (1989), 247-261.
    • (1989) Inform. and Comput. , vol.82 , Issue.3 , pp. 247-261
    • Ehrenfeucht, A.1    Haussler, D.2    Kearns, M.3    Valiant, L.4
  • 9
    • 0002192516 scopus 로고
    • Decision theoretic generalizations of the PAC model for neural net and other learning applications
    • D. Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Inform. and Comput. 100, No. 1 (1992), 78-150.
    • (1992) Inform. and Comput. , vol.100 , Issue.1 , pp. 78-150
    • Haussler, D.1
  • 11
    • 85029982621 scopus 로고    scopus 로고
    • to appear
    • M. J. Kearns and R. E. Schapire, Efficient distribution-free learning of probabilistic concepts, in "Proceedings, 31th Annual Symposium on the Foundations of Computer Science," pp. 382-392; J. Comput. System Sci., to appear.
    • J. Comput. System Sci.
  • 13
    • 0000378526 scopus 로고
    • On learning sets and functions
    • B. K. Natarajan, On learning sets and functions, Mach. Learning 4, No. 1 (1989), 67-97.
    • (1989) Mach. Learning , vol.4 , Issue.1 , pp. 67-97
    • Natarajan, B.K.1
  • 15
    • 1442267080 scopus 로고
    • Learning decision lists
    • R. Rivest, Learning decision lists, Mach. Learning 2, No. 3 (1987), 229-246.
    • (1987) Mach. Learning , vol.2 , Issue.3 , pp. 229-246
    • Rivest, R.1
  • 16
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L. G. Valiant, A theory of the learnable, Commun. ACM 27, No. 11 (1984), 1134-1142.
    • (1984) Commun. ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1
  • 17
    • 0000819141 scopus 로고
    • A learning criterion for stochastic rules
    • Yamanishi, A learning criterion for stochastic rules, Mach. Learning 9 (1992), 165-203.
    • (1992) Mach. Learning , vol.9 , pp. 165-203
    • Yamanishi1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.