References









A good starting point to read about SD is a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE-PAMI), titled "On the Algorithmic Implementation of Stochastic Discrimination". A PDF version of this paper is available online (about 1.4MB download).
Related work and publications referenced elsewhere on this site are as follows:
  1. R. Berlind, An Alternative Method of Stochastic Discrimination with Applications to Pattern Recognition, Ph.D. Thesis, SUNY/Buffalo, 1994.

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

  3. D. Chen, Statistical Estimates for Kleinberg's Method of Stochastic Discrimination, Ph.D. Thesis, SUNY/Buffalo, 1998.

  4. 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.

  5. 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.

  6. 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.

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

  8. E. M. Kleinberg, Stochastic Discrimination, Annals of Mathematics and Artificial Intelligence, 1990, pp. 207-239.

  9. E. M. Kleinberg, An Overtraining-Resistant Stochastic Modeling Method for Pattern Recognition, Annals of Statistics, 1996, pp. 2319-2349.

  10. E. M. Kleinberg, A Mathematically Rigorous Foundation for Supervised Learning, to appear in Proc. of the First International Workshop on Multiple Classifier Systems, Caligari, Italy, June, 2000.

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

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

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

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

  15. V. N. Vapnik, Estimation of Dependences Based on Empirical Data, Springer-Verlag, 1982.
kleinbrg@math.buffalo.edu
Last modified on Saturday, 25-Feb-2006 18:37:48 EST