Related work and publications are as follows:
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• D. Chen, Statistical Estimates for Kleinberg's Method of Stochastic Discrimination, Ph.D. Thesis, SUNY/Buffalo, 1998.

• E. M. Kleinberg, On the Algorithmic Implementation of Stochastic Discrimination, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 5, pp. 473-490, May 2000
  A PDF version of this paper is available online (about 1.4MB download)

• E. M. Kleinberg, A Mathematically Rigorous Foundation for Supervised Learning, Proc. of the First International Workshop on Multiple Classifier Systems, Caligari, Italy, June, 2000, published as Springer-Verlag Lecture Notes in Computer Science, Vol. 1857.
  A PDF version of this paper is available online (about 200KB download)

• E. M. Kleinberg, Stochastic Discrimination, Annals of Mathematics and Artificial Intelligence, 1990, pp. 207-239.
  A PDF version of this paper is available online (about 140KB download)

• E. M. Kleinberg, An Overtraining-Resistant Stochastic Modeling Method for Pattern Recognition, Annals of Statistics, 1996, pp. 2319-2349.
  A PDF version of this paper is available online (about 1.4MB download)

• E. M. Kleinberg, A Note on the Mathematics Underlying Boosting, preprint, to appear.

• Y. Freund, R. E. Schapire, Experiments with a New Boosting Algorithm, Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, July 3-6, 1996, pp. 148-156.

• Y. Freund, R. E. Schapire, A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting, Journal of Computer and System Sciences, 1997, pp. 119-139.

• R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, Oct 1993.

• J. Ross Quinlan, Boosting, Bagging and C4.5, AAAI 1996.

• L. Breiman, Bagging Predictors, Machine Learning, 24, 1996, pp. 123-140.

• D. Michie, D. Spiegelhalter, C. C. Taylor, Machine Learning, Neural and Statistical Classification, Ellis Horwood, 1994.

• R. Berlind, An Alternative Method of Stochastic Discrimination with Applications to Pattern Recognition, Ph.D. Thesis, SUNY/Buffalo, 1994.

• T. K. Ho, Random Decision Forests, Proc. of the 3rd Int'l Conference on Document Analysis and Recognition, Montreal, Canada, 1995, pp. 278-282.

• T. K. Ho, The Random Subspace Method for Constructing Decision Forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, August, 1998, pp. 832-844.