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Volumn 2, Issue 5, 2005, Pages 559-563

Cooperative evolutive concept learning: An empirical study

Author keywords

Concept learning; Cooperative learning; Genetic algorithms

Indexed keywords

COMPUTATIONAL COMPLEXITY; ERROR ANALYSIS; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS;

EID: 21944453105     PISSN: 17900832     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (26)

References (24)
  • 1
    • 0027696338 scopus 로고
    • Using genetic algorithms for concept learning
    • K. A. De Jong, W. M. Spears, and F. D. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13:161-188, 1993.
    • (1993) Machine Learning , vol.13 , pp. 161-188
    • Jong, K.A.1    Spears, W.M.2    Gordon, F.D.3
  • 2
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
    • T. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. 0Machine Learning, 40:139-158, 2000.
    • (2000) Machine Learning , vol.40 , pp. 139-158
    • Dietterich, T.1
  • 3
    • 0002032320 scopus 로고
    • A comparative review of selected methods for learning from examples
    • J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Morgan Kaufmann
    • T.G. Dietterich and R.S. Michalski. A comparative review of selected methods for learning from examples. In J.G. Carbonell, R.S. Michalski, and T. Mitchell, editors, Machine Learning, an Artificial Intelligence Approach. Morgan Kaufmann, 1983.
    • (1983) Machine Learning, an Artificial Intelligence Approach
    • Dietterich, T.G.1    Michalski, R.S.2
  • 4
    • 0000662737 scopus 로고
    • Search-intensive concept induction
    • A. Giordana and F. Neri. Search-intensive concept induction. Evolutionary Computation, 3 (4):375-416, 1995.
    • (1995) Evolutionary Computation , vol.3 , Issue.4 , pp. 375-416
    • Giordana, A.1    Neri, F.2
  • 7
    • 0000157651 scopus 로고
    • Co-evolving parasites improve simulated evolution as an optimization procedure
    • Christopher G. Langton, Charles Taylor, J. Doyne Farmer, and Steen Rasmussen, editors, Addison-Wesley, Santa Fe Institute, New Mexico, USA, 1990
    • W. Daniel Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In Christopher G. Langton, Charles Taylor, J. Doyne Farmer, and Steen Rasmussen, editors, Artificial Life II, volume X, pages 313-324. Addison-Wesley, Santa Fe Institute, New Mexico, USA, 1990 1992.
    • (1992) Artificial Life II , vol.10 , pp. 313-324
    • Hillis, W.D.1
  • 10
    • 0001957362 scopus 로고
    • A theoretical investigation of a parallel genetic algorithm
    • Fairfax, VA, Morgan Kaufmann
    • P. Husbands and F. Mill. A theoretical investigation of a parallel genetic algorithm. In Fourth International Conference on Genetic Algorithms, pages 264-270, Fairfax, VA, 1991. Morgan Kaufmann.
    • (1991) Fourth International Conference on Genetic Algorithms , pp. 264-270
    • Husbands, P.1    Mill, F.2
  • 11
    • 0027696178 scopus 로고
    • A knowledge intensive genetic algorithm for supervised learning
    • C.Z. Janikow. A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198-228, 1993.
    • (1993) Machine Learning , vol.13 , pp. 198-228
    • Janikow, C.Z.1
  • 13
    • 85141038051 scopus 로고    scopus 로고
    • Mining audit data to build intrusion detection models
    • Fairfax, VA
    • W. Lee, S. Stolfo, and K. W. Mok. Mining audit data to build intrusion detection models. In Knowledge discovery in databases 1998, pages 66-72, Fairfax, VA, 1998.
    • (1998) Knowledge Discovery in Databases 1998 , pp. 66-72
    • Lee, W.1    Stolfo, S.2    Mok, K.W.3
  • 14
    • 85005299854 scopus 로고
    • The multi-purpose incremental learning system AQ15 and its testing application to three medical domains
    • Philadelphia, PA
    • R. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In Fifth National Conference on Artificial Intelligence, pages 1041-1045, Philadelphia, PA, 1986.
    • (1986) Fifth National Conference on Artificial Intelligence , pp. 1041-1045
    • Michalski, R.1    Mozetic, I.2    Hong, J.3    Lavrac, N.4
  • 16
    • 21944443806 scopus 로고    scopus 로고
    • Comparing local search with respect to genetic evolution to detect intrusions in computer networks
    • IEEE Press
    • F. Neri. Comparing local search with respect to genetic evolution to detect intrusions in computer networks. In Congress on Evolutionary Computation 2000, pages 512-517, IEEE Press, 2000.
    • (2000) Congress on Evolutionary Computation 2000 , pp. 512-517
    • Neri, F.1
  • 17
  • 19
    • 0001172265 scopus 로고
    • Learning logical definitions from relations
    • J. R. Quinlan. Learning logical definitions from relations. Machine Learning, 5:239-266, 1990.
    • (1990) Machine Learning , vol.5 , pp. 239-266
    • Quinlan, J.R.1
  • 21
    • 21944451096 scopus 로고
    • Oversearching and layered search in empirical learning
    • Lake Tahoe, CA
    • R. Quinlan. Oversearching and layered search in empirical learning. In International Conference on Machine Learning, Lake Tahoe, CA, 1995.
    • (1995) International Conference on Machine Learning
    • Quinlan, R.1
  • 22
    • 84880692052 scopus 로고    scopus 로고
    • A brief introduction to boosting
    • R. E. Schapire. A brief introduction to boosting, pages 1401-1406, 1999.
    • (1999) , pp. 1401-1406
    • Schapire, R.E.1
  • 23
    • 0004161512 scopus 로고
    • Concept acquisition through representational adjustement
    • Technical Report TR 87-19, Dept. of Information and Computer Science, University of Californina, Irvine, CA
    • J. S. Schlimmer. Concept acquisition through representational adjustement. Technical Report TR 87-19, Dept. of Information and Computer Science, University of Californina, Irvine, CA, 1987.
    • (1987)
    • Schlimmer, J.S.1
  • 24
    • 2542590449 scopus 로고    scopus 로고
    • Does data-mod co-evolution improve generalization performances of evolving learners?
    • LNCS 1498
    • J. L. Shapiro. Does data-mod co-evolution improve generalization performances of evolving learners? Lecture Notes in Computer Science, LNCS 1498:540-549, 1998.
    • (1998) Lecture Notes in Computer Science , pp. 540-549
    • Shapiro, J.L.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.