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Volumn 1995-January, Issue , 1995, Pages 329-336

Concept learning with geometric hypotheses

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY;

EID: 84961365937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/225298.225338     Document Type: Conference Paper
Times cited : (16)

References (22)
  • 1
    • 0000492326 scopus 로고
    • Learning from noisy examples
    • D. Angluin and P. Laird, Learning from noisy examples. Machine Learning, 2(1988), 343-370.
    • (1988) Machine Learning , vol.2 , pp. 343-370
    • Angluin, D.1    Laird, P.2
  • 2
    • 0002117591 scopus 로고
    • A further comparison of splitting rules for decision-tree induction
    • W. Buntine and T. Niblett, A further comparison of splitting rules for decision-tree induction. Machine Learning, 8(1992), 75-82.
    • (1992) Machine Learning , vol.8 , pp. 75-82
    • Buntine, W.1    Niblett, T.2
  • 4
    • 85050914674 scopus 로고
    • Computing half-plane and strip discrepancy of planar point sets
    • M. deBerg, Computing Half-Plane and Strip Discrepancy of Planar Point Sets. Manuscript, (1994).
    • (1994) Manuscript
    • DeBerg, M.1
  • 6
    • 84947986089 scopus 로고    scopus 로고
    • Computing the maximum bichromatic discrepancy, with applications in computer graphics and machine learning
    • to appear
    • D. Dobkin, D. Gunopulos and W. Maass, Computing the maximum Bichromatic Discrepancy, with applications in Computer Graphics and Machine Learning. JCSS, to appear.
    • JCSS
    • Dobkin, D.1    Gunopulos, D.2    Maass, W.3
  • 7
    • 0024606073 scopus 로고
    • Topologically sweeping an arrangement
    • H. Edelsbrunner and L. J. Guibas, Topologically Sweeping an Arrangement. JCSS, 38, 165-194 (1989).
    • (1989) JCSS , vol.38 , pp. 165-194
    • Edelsbrunner, H.1    Guibas, L.J.2
  • 8
    • 85050931021 scopus 로고
    • Learning unions of convex polygons
    • P. Fisher, Learning unions of convex polygons. Proc. of EURO-COLT'93, 1993.
    • (1993) Proc. of EURO-COLT'93
    • Fisher, P.1
  • 9
    • 85050936894 scopus 로고    scopus 로고
    • More or less efficient agnostic learning of convex polygons
    • to appear
    • P. Fisher, More or Less Efficient Agnostic Learning of Convex Polygons, these proceedings (COLT-95), to appear.
    • These Proceedings (COLT-95)
    • Fisher, P.1
  • 10
    • 0002192516 scopus 로고
    • Decision theoretic generations of the PAC-model for neural nets and other applications
    • D. Haussler, Decision theoretic generations of the PAC-model for neural nets and other applications. Inf. and Comp., 100(1992), 78-150.
    • (1992) Inf. and Comp. , vol.100 , pp. 78-150
    • Haussler, D.1
  • 11
    • 0027580356 scopus 로고
    • Very simple classification rules perform well on most commonly used datasets
    • R. C. Holte, Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1993), 63-91.
    • (1993) Machine Learning , vol.11 , pp. 63-91
    • Holte, R.C.1
  • 17
    • 0021518106 scopus 로고
    • A theory of the learnable
    • L. G. Valiant, A theory of the learnable. Coram, of the ACM 27(1984), 1134-1142.
    • (1984) Coram, of the ACM , vol.27 , pp. 1134-1142
    • Valiant, L.G.1
  • 18
    • 0001024505 scopus 로고
    • Chervonenkis, on the uniform convergence of relative frequencies of events to their probabilities
    • V. N. Vapnik and A. Ya. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Applic. 16(1971), 264-280.
    • (1971) Theory Probab. Applic. , vol.16 , pp. 264-280
    • Vapnik, V.N.1    Ya, A.2
  • 19
    • 0025492125 scopus 로고
    • Maximizing the predictive value of production rules
    • S. M. Weiss, R. Galen and P. V. Tadepalli, Maximizing the predictive value of production rules. Art. Int. 45(1990), 47-71.
    • (1990) Art. Int. , vol.45 , pp. 47-71
    • Weiss, S.M.1    Galen, R.2    Tadepalli, P.V.3
  • 20
    • 0001512820 scopus 로고
    • An empirical comparison of pattern recognition, neural nets, and machine learning classification methods
    • Morgan Kauffmann
    • S. M. Weiss and I. Kapouleas, An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. 11th Int. Joint Conf. on Art. Int. (1990), Morgan Kauffmann, 781-787.
    • (1990) 11th Int. Joint Conf. on Art. Int , pp. 781-787
    • Weiss, S.M.1    Kapouleas, I.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.