The Synergy Between PAV and AdaBoost (original) (raw)
References
Apte, C., Damerau, F., & Weiss, S. (1998). Text mining with decision rules and decision trees. Conference Proceedings The Conference on Automated Learning and Discovery, CMU.
Aslam, J. (2000). Improving algorithms for boosting. Conference Proceedings 13th COLT. Palo Alto, California.
Ayer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., & Silverman, E. (1954). An empirical distribution function for sampling with incomplete information. Annals of Mathematical Statistics, 26, 641–647. MathSciNet Google Scholar
Bennett, K. P., Demiriz, A., & Shawe-Taylor, J. (2000). A column generation algorithm for boosting. Conference Proceedings 17th ICML.
Buja, A., Hastie, T., & Tibshirani, R. (1989). Linear smoothers and additive models. The Annals of Statistics, 17:2 453–555. MathSciNet Google Scholar
Burges, C. J. C. (1999). A tutorial on support vector machines for pattern recognition (Available electronically from the author): Bell Laboratories, Lucent Technologies.
Carreras, X., & Marquez, L. (2001). September 5–7, 2001. Boosting trees for anti-spam email filtering. Conference Proceedings RANLP2001, Tzigov Chark, Bulgaria.
Collins, M., Schapire, R. E., & Singer, Y. (2002). Logistic regression, AdaBoost and Bregman distances. Machine Learning, 48:1, 253–285. Article Google Scholar
Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern Classification (2 edn.). New York: John Wiley & Sons, Inc. Google Scholar
Duffy, N., & Helmbold, D. (1999). Potential boosters? Conference Proceedings Advances in Neural Information Processing Systems 11.
Duffy, N., & Helmbold, D. (2000). Leveraging for regression. Conference Proceedings 13th Annual Conference on Computational Learning Theory. San Francisco.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal or Computer and System Sciences, 55:1, 119–139. MathSciNet Google Scholar
Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 38:2, 337–374. MathSciNet Google Scholar
Hardle, W. (1991). Smoothing Techniques: With Implementation in S. New York: Springer-Verlag. Google Scholar
Johnson, M., Geman, S., Canon, S., Chi, Z., & Riezler, S. (1999). Estimators for stochastic “unification-based” grammars. Conference Proceedings Proceedings ACL'99. Univ. Maryland.
Kim, W., Aronson, A. R., & Wilbur, W. J. (2001). Automatic MeSH term assignment and quality assessment. Conference Proceedings Proc. AMIA Symp. Washington, D.C.
Kim, W. G., & Wilbur, W. J. (2001). Corpus-based statistical screening for content-bearing terms. Journal of the American Society for Information Science, 52:3, 247–259. Google Scholar
Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. Conference Proceedings Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA.
Maclin, R. (1998). Boosting classifiers locally. Conference Proceedings Proceedings of AAAI.
Mason, L., Bartlett, P. L., & Baxter, J. (2000). Improved generalizations through explicit optimizations of margins. Machine Learning, 38, 243–255. Article Google Scholar
McCallum, A., & Nigam, K. (1998). A comparison of event models for naive bayes text classification. Conference Proceedings AAAI-98 Workshop on Learning for Text Categorization.
Meir, R., El-Yaniv, R., & Ben-David, S. 2000. Localized boosting. Conference Proceedings 13th COLT. Palo Alto, California.
Mitchell, T. M. (1997). Machine learning. Boston: WCB/McGraw-Hill. Google Scholar
Moerland, P., & Mayoraz, E. (1999). DynamBoost: combining boosted hypotheses in a dynamic way (Technical Report RR 99-09): IDIAP Switzerland.
Nock, R., & Sebban, M. (2001). A Bayesian boosting theorem. Pattern Recognition Letters, 22, 413–419. Google Scholar
Pardalos, P. M., & Xue, G. (1999). Algorithms for a class of isotonic regression problems. Algorithmica, 23, 211–222. MathSciNet Google Scholar
Ratsch, G., Mika, S., & Warmuth, M. K. (2001).On the Convergence of Leveraging(NeuroCOLT2 Technical Report 98). London: Royal Holloway College. Google Scholar
Ratsch, G., Onoda, T., & Muller, K.-R. (2001). Soft margins for AdaBoost. Machine Learning, 42, 287–320. Google Scholar
Robertson, S. E., & Walker, S. (1994). Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval. Conference Proceedings 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37:3, 297–336. Article Google Scholar
Vapnik, V. (1998). Statistical Learning Theory. New York: John Wiley & Sons, Inc. Google Scholar
Witten, I. H., Moffat, A., & Bell, T. C. (1999). Managing Gigabytes (2 edn.). San Francisco: Morgan-Kaufmann Publishers, Inc. Google Scholar
Zhang, T., & Oles, F. J. (2001). Text categorization based on regularized linear classification methods. Information Retrieval, 4:1, 5–31. Google Scholar