Improved boosting algorithms using confidence-rated predictions

Machine Learning

Volumn 37, Issue 3, 1999, Pages 297-336

  Schapire, Robert E ^a   Singer, Yoram ^a,b  

^a AT AND T LABS RESEARCH   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; ENCODING (SYMBOLS); TREES (MATHEMATICS);

BOOSTING ALGORITHMS; DECISION TREES; MULTICLASS CLASSIFICATION;

ALGORITHMS;

EID: 0033281701     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007614523901     Document Type: Article

References (26)

1. Bartlett, P.L. (1998). The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 44(2), 525-536.
2. Bauer, E., & Kohavi, R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1/2):105-139, 1999.
3. Baum, E.B., & Haussler, D. (1989). What size net gives valid generalization? Neural Computation, 1(1), 151-160.
4. Blum, A. (1997). Empirical support for winnow and weighted-majority based algorithms: results on a calendar scheduling domain. Machine Learning, 26, 5-23.
5. Breiman, L. (1998). Arcing classifiers. The Annals of Statistics, 26(3), 801-849.
6. Csiszár, I., & Tusnády, G. (1984). Information geometry and alternaning minimization procedures. Statistics and Decisions, Supplement Issue, 1, 205-237.
7. Dietterich, T.G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, to appear.
8. Dietterich, T.G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286.
9. Drucker, H., & Cortes, C. (1996). Boosting decision trees. In Advances in Neural Information Processing Systems, 8, MIT Press.
10. Fletcher, R. (1987). Practical Methods of Optimization (second edition), John Wiley.
11. Freund, Y., Iyer, R., Schapire, R.E., & Singer, Y. (1998). An efficient boosting algorithm for combining preferences. Machine Learning: Proceedings of the Fifteenth International Conference.
12. Freund, Y., & Schapire, R.E. (1996). Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference (pp. 148-156).
13. Freund, Y., & Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119-139.
14. Freund, Y., Schapire, R.E., Singer, Y., & Warmuth, M.K. (1997). Using and combining predictors that specialize. Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing (pp. 334-343).
15. Friedman, J., Hastie, T., & Tibshirani, R. (1998). Additive logistic regression: A statistical view of boosting Technical Report.
16. Haussler, D. (1992). Decision theoretic generalizations of the PAC model for neural net and other learning applications Information and Computation, 100(1), 78-150.
17. Haussler, D., & Long, P.M. (1995). A generalization of Sauer's lemma. Journal of Combinatorial Theory, Series A, 71(2), 219-240.
18. Kearns, M., & Mansour, Y. (1996). On the boosting ability of top-down decision tree learning algorithms. Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing.
19. Maclin, R., & Opitz, D. (1997). An empirical evaluation of bagging and boosting. Proceedings of the Fourteenth National Conference on Artificial Intelligence (pp. 546-551).
20. Margineantu, D.D., & Dietterich, T.G. (1997). Pruning adaptive boosting. Machine Learning: Proceedings of the Fourteenth International Conference (pp. 211-218).
21. Merz, C.J., & Murphy, P.M. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/ ~mlearn/MLRepository.html.
22. Quinlan, J.R. (1996). Bagging, boosting, and C4.5. Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 725-730).
23. Schapire, R.E. (1997). Using output codesto boost multiclass learning problems. Machine Learning: Proceedings of the Fourteenth International Conference (pp. 313-321).
24. Schapire, R.E., Freund, Y., Bartlett, P., & Lee, W.S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics, 26(5), 1651-1686.
25. Schapire, R.E., & Singer, Y. BoosTexter: A boosting-based system for text categorization. Machine Learning, to appear.
26. Schwenk, H., & Bengio, Y. (1998). Training methods for adaptive boosting of neural networks. In Advances in Neural Information Processing Systems 10. MIT Press.