Convex hull ensemble machine

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2002, Pages 243-249

  Kim, Yongdai ^a  

^a Ewha Womans University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 62649083059     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. E. Bauer and R. Kohavi, "An empirical comparison of voting classification algorithms: Bagging, Boosting and Variants". Machine Learning, 1999, pp. 105-139.
2. L. Breiman, "Bagging predictors", Machine Learning. 1996, pp. 123-140
3. L. Breiman, "Arcing classifiers", Annals of Statistics. 1998, pp. 801-846.
4. L. Breiman, "Random forests". Machine Learning, 2001, pp. 5-32.
5. 2 loss: regression and classification". Technical Report, 2001.
6. T.G. Dietterich, "An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting and Randomization", Machine Learning, 2000, pp. 139-157.
7. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting", Journal of Computer and System Sciences, 1997, pp. 119-139.
8. J.H. Friedman, "Multivariate adaptive regression splines (with discussion)", it Annals of Statistics, 1991, pp.1-141.
9. J.H. Friedman, "Greedy function approximation : a gradient boosting machine", Annals of Statistics, 2001, pp/1189-1232.
10. J.H. Friedman, T. Hastie and R. Tibshirani, "Additive logistic regression : a statistical view of boosting", Annals of Statistics, 2000, pp. 337-374.
11. W. Jiang, "On weak base hypotheses and their implications for boosting regression classification", Annals of Statistics, 2002, pp.51-73.
12. Y. Kim, J. Kim and W. Jan, "Convex Hull Ensemble Machine". Unpublished manuscript at http://home.ewha.ac.kr/ydkim, 2002.
13. D. Opitz and R. Maclin, "Popular ensemble methods: An empirical study", Journal of Artificial Intelligence Research, 1999, pp. 169-198.
14. J. Quinlan, "Boosting first-order learning", In S. Arikawa & Sharma Eds.), LNAI, Vol. 1160: Proceedings of the 7th International Workshop on Algorithmic Learning Theory 143-155, Berlin : Springer.
15. G. Räsch, T. Onoda and K.R. Müller, "Soft margins for AdaBoost", Machine Learning, 2001, pp. 287-320.
16. G. Ridgeway, "Contribution to the discussion of paper by Friedman, Hastie and Tibshirani", Ann. Statist., 2000, pp. 393-400.
17. R. Schapire, Y. Freund, P. Bartlett and W. Lee, "Boosting the margin: a new explanation for the effectiveness of voting methods", Ann. Statist., 1998, pp. 1651-1686.
18. R. Schapire and Y. Singer, "Improved boosting algorithms using confidence-rated predictions", Machine Learning, 1999, pp. 297-336.