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Volumn , Issue , 2006, Pages 878-882

A probabilistic ensemble pruning algorithm

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; MAXIMUM PRINCIPLE;

EID: 40949161246     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icdmw.2006.18     Document Type: Conference Paper
Times cited : (21)

References (12)
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 5
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • J. H. Friedman. Multivariate adaptive regression splines. he Annals of Statistics, 19(1):1-141, 1991.
    • (1991) He Annals of Statistics , vol.19 , Issue.1 , pp. 1-141
    • Friedman, J.H.1
  • 7
    • 0042525838 scopus 로고    scopus 로고
    • A constructive algorithm for training cooperative neural network ensembles
    • M. M. Islam, X. Yao, and K. Murase. A constructive algorithm for training cooperative neural network ensembles. IEEE Transaction on Neural Networks, 14(4):820-834, 2003.
    • (2003) IEEE Transaction on Neural Networks , vol.14 , Issue.4 , pp. 820-834
    • Islam, M.M.1    Yao, X.2    Murase, K.3
  • 8
    • 0033485370 scopus 로고    scopus 로고
    • Ensemble learning via negative correlation
    • Y. Liu and X. Yao. Ensemble learning via negative correlation. Neural Networks, 12(10):1399-1404, 1999.
    • (1999) Neural Networks , vol.12 , Issue.10 , pp. 1399-1404
    • Liu, Y.1    Yao, X.2
  • 11
    • 0001224048 scopus 로고    scopus 로고
    • Sparse bayesian learning and the relevance vector machine
    • M. E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001.
    • (2001) Journal of Machine Learning Research , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 12
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • Z. Zhou, J. Wu, and W. Tang. Ensembling neural networks: many could be better than all. Artificial Intelligence, 137(1-2):239-263, 2002.
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 239-263
    • Zhou, Z.1    Wu, J.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.