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.

MathSciNetBennett, 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.

MathSciNetBurges, 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.

CrossRefDuda, R. O., Hart, P. E., & Stork, D. G. (2000). *Pattern Classification* (2 edn.). New York: John Wiley & Sons, Inc.

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.

MathSciNetFriedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting.

*The Annals of Statistics*,

*38*:2, 337–374.

MathSciNetHardle, W. (1991). *Smoothing Techniques: With Implementation in* S. New York: Springer-Verlag.

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.

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.

CrossRefMcCallum, 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.

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.

Pardalos, P. M., & Xue, G. (1999). Algorithms for a class of isotonic regression problems.

*Algorithmica*,

*23*, 211–222.

MathSciNetRatsch, G., Mika, S., & Warmuth, M. K. (2001).*On the Convergence of Leveraging* *(NeuroCOLT2 Technical Report 98)*. London: Royal Holloway College.

Ratsch, G., Onoda, T., & Muller, K.-R. (2001). Soft margins for AdaBoost. *Machine Learning*, *42*, 287–320.

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.

CrossRefVapnik, V. (1998). *Statistical Learning Theory*. New York: John Wiley & Sons, Inc.

Witten, I. H., Moffat, A., & Bell, T. C. (1999). *Managing Gigabytes (2 edn.)*. San Francisco: Morgan-Kaufmann Publishers, Inc.

Zhang, T., & Oles, F. J. (2001). Text categorization based on regularized linear classification methods. *Information Retrieval*, *4*:1, 5–31.