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Volumn 1574, Issue , 1999, Pages 296-305

Improving the performance of boosting for naive bayesian classication

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; TREES (MATHEMATICS);

EID: 84947742420     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48912-6_41     Document Type: Conference Paper
Times cited : (25)

References (17)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer, E. & Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. To appear in Machine Learning, (1999).
    • (1999) Machine Learning
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0003408496 scopus 로고    scopus 로고
    • Irvine, CA: University of California, Department of Information and Computer Science
    • Blake, C., Keogh, E. & Merz, C.J.: UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science. (1998).
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.1    Keogh, E.2    Merz, C.J.3
  • 3
    • 0003619255 scopus 로고    scopus 로고
    • Bias, variance, and arcing classifiers
    • Department of Statistics, University of California, Berkeley, CA
    • Breiman, L.: Bias, variance, and arcing classifiers. Technical Report 460, Department of Statistics, University of California, Berkeley, CA. (1996).
    • (1996) Technical Report 460
    • Breiman, L.1
  • 6
    • 0002419948 scopus 로고    scopus 로고
    • Beyond independence: Conditions for the optimality of the simple Bayesian classifier
    • San Francisco, CA: Morgan Kaufmann
    • Domingos, P. & Pazzani, M.: Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Proceedings of the Thirteenth International Conference on Machine Learning. (1996) 105-112. San Francisco, CA: Morgan Kaufmann.
    • (1996) Proceedings of the Thirteenth International Conference on Machine Learning , pp. 105-112
    • Domingos, P.1    Pazzani, M.2
  • 11
    • 0003112380 scopus 로고
    • Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition
    • B. Wielinga et al. (eds.), Amsterdam: IOS Press
    • Kononenko, I.: Comparison of inductive and naive Bayesian learning approaches to automatic knowledge acquisition. In B. Wielinga et al. (eds.), Current Trends in Knowledge Acguisition. Amsterdam: IOS Press. (1990).
    • (1990) Current Trends in Knowledge Acguisition
    • Kononenko, I.1
  • 17
    • 84947737993 scopus 로고    scopus 로고
    • Improving the performance of boosting for naive Bayesian classification
    • Deakin University
    • Ting, K.M. & Zheng, Z.: Improving the performance of boosting for naive Bayesian classification. TR C99/01, School of Computing and Mathematics, Deakin University. (1999). [http://www3.cm.deakin.edu.au/~kmting].
    • (1999) TR C99/01, School of Computing and Mathematics
    • Ting, K.M.1    Zheng, Z.2


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