메뉴 건너뛰기




Volumn 3003, Issue , 2004, Pages 47-56

Boosting technique for combining cellular GP classifiers

Author keywords

[No Author keywords available]

Indexed keywords

GENETIC ALGORITHMS; GENETIC PROGRAMMING;

EID: 35048860467     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24650-3_5     Document Type: Article
Times cited : (11)

References (20)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, (36):105-139, 1999.
    • (1999) Machine Learning , Issue.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Leo Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 3
  • 5
  • 7
    • 0002344899 scopus 로고    scopus 로고
    • A genetic programming framework for two data mining tasks: Classification and generalised rule induction
    • Stanford University, CA, USA
    • A.A. Freitas. A genetic programming framework for two data mining tasks: Classification and generalised rule induction. In Proceedings of the 2nd Int. Conference on Genetic Programming, pages 96-101. Stanford University, CA, USA, 1997.
    • (1997) Proceedings of the 2nd Int. Conference on Genetic Programming , pp. 96-101
    • Freitas, A.A.1
  • 9
    • 0001501920 scopus 로고    scopus 로고
    • Bagging, boosting, and bloating in genetic programming
    • Orlando, Florida, July Morgan Kaufmann
    • Hitoshi Iba. Bagging, boosting, and bloating in genetic programming. In Proc. Of the Genetic and Evolutionary Computation Conference GECCO99, pages 1053-1060, Orlando, Florida, July 1999. Morgan Kaufmann.
    • (1999) Proc. of the Genetic and Evolutionary Computation Conference GECCO99 , pp. 1053-1060
    • Iba, H.1
  • 16
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • R. E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197-227, 1990.
    • (1990) Machine Learning , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.E.1
  • 17
    • 0006294849 scopus 로고    scopus 로고
    • Parallel out-of-core decision tree classifiers
    • H. Kargupta and P. Chan, editors, Menlo Park, AAAI Press
    • M. Sreenivas, K. AlSabti, and S. Ranka. Parallel out-of-core decision tree classifiers. In H. Kargupta and P. Chan, editors, in Advances in Distributed Data Mining, Menlo Park, 1996. AAAI Press.
    • (1996) Advances in Distributed Data Mining
    • Sreenivas, M.1    Alsabti, K.2    Ranka, S.3
  • 19
    • 0000473062 scopus 로고    scopus 로고
    • Parallel and distributed evolutionary algorithms: A review
    • P. Neittaanmäki, K. Miettinen, M. Mäkelä and J. Periaux, editors, J. Wiley and Sons, Chichester
    • M. Tomassini. Parallel and distributed evolutionary algorithms: A review. In P. Neittaanmäki, K. Miettinen, M. Mäkelä and J. Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, J. Wiley and Sons, Chichester, 1999.
    • (1999) Evolutionary Algorithms in Engineering and Computer Science
    • Tomassini, M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.