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Volumn 1, Issue , 1999, Pages 212-217

A genetic constructive induction model

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS;

EID: 84901420315     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.1999.781928     Document Type: Conference Paper
Times cited : (12)

References (14)
  • 2
    • 0003578240 scopus 로고
    • An empirical study of learning speed in back-propagation networks
    • Carnegie-Mellon, Computer Science Dept.
    • S. E. Fahlman. An empirical study of learning speed in back-propagation networks. Technical Report CMU-CS-88-162, Carnegie-Mellon, Computer Science Dept., 1988.
    • (1988) Technical Report CMU-CS-88-162
    • Fahlman, S.E.1
  • 3
    • 85008255983 scopus 로고
    • Dynamic training subset selection for supervised learning in genetic programming
    • Yuval Davidor, Hans-Paul Schwefel, and Reinhard Männer, editors, Jerusalem, 9-14 October, Springer-Verlag
    • Chris Gathercole and Peter Ross. Dynamic training subset selection for supervised learning in genetic programming. In Yuval Davidor, Hans-Paul Schwefel, and Reinhard Männer, editors. Parallel Problem Solving from Nature III, pages 312-321, Jerusalem, 9-14 October 1994. Springer-Verlag.
    • (1994) Parallel Problem Solving from Nature III , pp. 312-321
    • Gathercole, C.1    Ross, P.2
  • 4
    • 0001851932 scopus 로고    scopus 로고
    • Tackling the boolean even N parity problem with genetic programming and limited-error fitness
    • John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Stanford University, CA, USA, 13-16 July, Morgan Kaufmann
    • Chris Gathercole and Peter Ross. Tackling the boolean even N parity problem with genetic programming and limited-error fitness. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors. Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 119-127, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.
    • (1997) Genetic Programming 1997: Proceedings of the Second Annual Conference , pp. 119-127
    • Gathercole, C.1    Ross, P.2
  • 7
    • 0013337451 scopus 로고
    • Feature construction: An analytic framework and an application to decision trees
    • Department of Computer Science, University of Illinois, Urbana, IL. USA
    • C Matheus. Feature construction: an analytic framework and an application to decision trees. Technical Report CSR-89-1559, Department of Computer Science, University of Illinois, Urbana, IL. USA, 1989.
    • (1989) Technical Report CSR-89-1559
    • Matheus, C.1
  • 8
    • 0025389210 scopus 로고
    • Boolean feature discovery in empirical learning
    • G. Pagallo and D. Haussler. Boolean feature discovery in empirical learning. Machine Learning, 5:71-100, 1990.
    • (1990) Machine Learning , vol.5 , pp. 71-100
    • Pagallo, G.1    Haussler, D.2
  • 10
    • 0000686085 scopus 로고
    • Learning hard concepts through constructive induction: Framework and rationale
    • L Rendell and R. Seshu. Learning hard concepts through constructive induction: Framework and rationale. Computational Intelligence, 9:247-270, 1990.
    • (1990) Computational Intelligence , vol.9 , pp. 247-270
    • Rendell, L.1    Seshu, R.2
  • 11
    • 0000646059 scopus 로고
    • Learning internal representations by error propagation
    • D. Rumelhart, J. McClelland, and the PDP Research Group, editors, Vols I and II. MIT Press, Cambridge, Mass.
    • D. Rumelhart, G. Hinton, and R. Williams. Learning internal representations by error propagation. In D. Rumelhart, J. McClelland, and the PDP Research Group, editors, Parallel Distributed Processing: Explorations in the Micro-structures of Cognition. Vols I and II. MIT Press, Cambridge, Mass., 1986.
    • (1986) Parallel Distributed Processing: Explorations in the Micro-structures of Cognition
    • Rumelhart, D.1    Hinton, G.2    Williams, R.3
  • 13
    • 0000599779 scopus 로고
    • Hypothesis-driven constructive induction in aql7: A method and experiments
    • J. Wnek and R. S. Michalski. Hypothesis-driven constructive induction in aql7: A method and experiments. Machine Learning, 14:139-169, 1994.
    • (1994) Machine Learning , vol.14 , pp. 139-169
    • Wnek, J.1    Michalski, R.S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.