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Improved boosting algorithms using confidence-rated predictions

Published: 24 July 1998 Publication History
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    References

    [1]
    Peter L. Bartlett. The sample complexity of pattem classification with neural networks: the size of the weights is more important than the size of the network. IEEE Transactions on Information Theory, 1998 (to appeaD.
    [2]
    Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Unpublished manuscript, 1997.
    [3]
    Eric B. Baum and David Haussler. What size net gives valid generalization? Neural Computation, 1(1): 151-160, 1989.
    [4]
    Avfim Blum. Empirical support for winnow and weightedmajority based algorithms: results on a calendar scheduling domain. In Proceedings of the Twelfth International Conj'krence on Machine Learning, pages 64-72, 1995.
    [5]
    Leo Breiman. Aming classifiers. Annals of Statistics, to appear.
    [6]
    Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, llnpublished manuscript, 1998.
    [7]
    Thomas G. Dietterich and Ghulum Bakiri. Solving multiclass learning problems via error-cmrecting output codes. Journal of Artificial Intelligence Research, 2:263-286, January 1995.
    [8]
    Hams Dmckerand Cofinna Cortes. Boosting decision trees. In Advances in Neural Information Processing Systems 8, pages 479-485, 1996.
    [9]
    Yoav Freund and Robert E. Schapire. F, xpefiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 148-156, 1996.
    [10]
    Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1): 119-139, August 1997.
    [11]
    Yoav Freund, Robert E. Schapire, Yoram Singer, and Manfred K. Wammth. Using and combining predictors that specialize. In Proceedings of the Twenty-Ninth Annual ACM Symposium on the Iheory of Computing, pages 334-343, 1997.
    [12]
    David Haussler. Decision theoretic generalizations of the PAC model for neural net and other leaming applications. Information and Computation, 100(1):78-150, 1992.
    [13]
    David Haussler and Philip M. Long. A generalization of Sauer's lemma. Journal of Combinatorial Theory, Series A, 71(2):219-240, 1995.
    [14]
    Michael Keams and Yishay Mansour. On the boosting ability of top-down decision tree learning algorithms. In Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, 1996.
    [15]
    Richard Maclin and David Opitz. An empirical evaluation of bagging and boosting. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pages 546- 551, 1997.
    [16]
    Dragos D. Margineantu and Thomas G. Dietterich. Pruning adaptive boosting. In Machine Learning: Proceedings of the Fourteenth International Conference, pages 211-218, 1997.
    [17]
    C. J. Merz and P. M. Murphy. UCI repository of machine leaming Databases,1998. http://www.ics .uci.edu/,mtleam/MLRe pository.html.
    [18]
    J. R. Quinlan. Bagging, boosting, and C4.5. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 725-730, 1996.
    [19]
    Robert E. Schapire. Using output codes to boost multiclass learning problems. In Machine Learning: Proceedings of the Fourteenth International Conference, pages 313-321, 1997.
    [20]
    Robert E. Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, to appear.
    [21]
    Robert E. Schapire and Yoram Singer. BoosTexter: A system for multiclass multi-label text categorization. Unpublished manuscript, 1998.
    [22]
    }Iolger Schwenk and Yoshua Bengio. Training methods for adaptive boosting of neural networks for character recognition. In Advances in Neural Information Processing Systems 10, 1998.

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    cover image ACM Conferences
    COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory
    July 1998
    304 pages
    ISBN:1581130570
    DOI:10.1145/279943
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    Published: 24 July 1998

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