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IBM Systems Journal  
Volume 41, Number 3, 2002
Artificial Intelligence
 Table of contents: arrowHTML arrowPDF arrowASCII   This article: arrowHTML arrowPDF arrowASCII arrowCopyright info
   

A probabilistic estimation framework for predictive modeling analytics - References
by C. V. Apte, R. Natarajan, E. P. D. Pednault and F. A. Tipu

Cited references

  1. C. Apte, E. Bibelnieks, R. Natarajan, E. P. D. Pednault, F. Tipu, D. Campbell, and B. Nelson, “Segmentation-Based Modeling for Advanced Targeted Marketing,” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), August 2001, ACM, New York (2001), pp. 408–413.
  2. C. Apte, E. Grossman, E. P. D. Pednault, B. Rosen, F. Tipu, and B. White, “Probabilistic Estimation-Based Data Mining for Discovering Insurance Risks,” IEEE Intelligent Systems 14, No. 6 (November/December 1999), pp. 49–58.
  3. R. Kohavi and G. H. John, “The Wrapper Approach,” Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers, New York (1998), pp. 33–50.
  4. J. R. Quinlan, “Learning with Continuous Classes,” Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, World Scientific Press, Singapore (1992), pp. 343–348.
  5. R. Kohavi, “Scaling Up the Accuracy of Naive Bayes Classifiers: A Decision-Tree Hybrid,” Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA (1996), pp. 202–207.
  6. G. V. Kass, “An Exploratory Technique for Investigating Large Quantities of Categorical Data,” Applied Statistics 29, No. 2, 119–127 (1980).
  7. G. H. Golub and C. F. Van Loan, Matrix Computations, Second Edition, Johns Hopkins University Press, Baltimore, MD (1989).
  8. A. Björck, Numerical Methods for Least Squares Problems, SIAM, Philadelphia, PA (1996).
  9. R. Natarajan and E. P. D. Pednault, “Segmented Regression Estimators for Massive Data Sets,” Proceedings of the 2nd SIAM International Conference on Data Mining, SIAM, Philadelphia, PA (2002).
  10. A. Agresti, Categorical Data Analysis, John Wiley & Sons, Inc., New York (1990).
  11. G. John and P. Langley, “Estimating Continuous Distributions in Bayesian Classifiers,” Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco, CA (1995), pp. 338–345.
  12. R. Kohavi and M. Sahimi, “Error-Based and Entropy-Based Discretization of Continuous Features,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA (1996), pp. 114–119.
  13. C. N. Hsu, J. J. Kuang, and T. T. Wong, “Why Discretization Works for Naive Bayesian Classifiers,” Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, CA (2000), pp. 399–406.
  14. R. Natarajan and E. P. D. Pednault, “Using Simulated Pseudo Data to Speed Up Statistical Predictive Modeling,” Proceedings of the First SIAM International Conference on Data Mining, SIAM, Philadelphia, PA (2001).
  15. G. Schwarz, “Estimating the Dimension of a Model,” Annals of Statistics 6, 461–464 (1978).
  16. P. Langley and S. Sage, “Induction of Selective Bayesian Classifiers,” Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco, CA (1994), pp. 399–406.
  17. S. A. Klugman, H. H. Panjer, and G. E. Wilmot, Loss Models: From Data to Decisions, John Wiley & Sons, Inc., New York (1998).
  18. E. P. D. Pednault and C. Apte, “Probabilistic Estimation in Data Mining,” Data Mining for Scientific and Engineering Applications, Kluwer Academic Publishing, New York (2001).
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