Technical note: Naive Bayes for regression

Machine Learning

Volumn 41, Issue 1, 2000, Pages 5-25

  Frank, Eibe ^a   Trigg, Leonard ^a   Holmes, Geoffrey ^a   Witten, Ian H ^a  

^a UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; MATHEMATICAL MODELS; MEASUREMENT ERRORS; PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS; STATISTICAL TESTS;

KERNEL DENSITY ESTIMATORS; LINEAR REGRESSION; MAXIMUM PROBABILITY; MODEL TREES; NAIVE BAYES LEARNING SCHEME;

NEURAL NETWORKS;

EID: 0034300831     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007670802811     Document Type: Article

References (32)

1. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the 2nd International Symposium on Information Theory (pp. 267-281). Budapest: Akadémiai Kiadó.
2. Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally weighted learning. Artificial Intelligence Review, 11, 11-73.
3. Blake, C., Keogh, E., & Merz, C. J. (1998). UCI repository of machine learning data-bases. Irvine, CA: University of California, Department of Information and Computer Science. [http://www.ics.uci.edu/˜mlearn/MLRepository.html].
4. Cestnik, B. (1990). Estimating probabilities: A crucial task in machine learning. In Proceedings of the 9th European Conference on Artificial Intelligence, Stockholm, Sweden (pp. 147-149). London: Pitman.
5. Clark, P. & Niblett, T. (1989). The CN2 Induction Algorithm. Machine Learning, 3(4), 261-283.
6. Domingos, P. & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. In Machine Learning, 29(2/3), 103-130.
7. Duda, R. & Hart, P. (1973). Pattern classification and scene analysis. New York: Wiley.
8. Fayyad, U. M. & Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France (pp. 1022-1027). San Mateo, CA: Morgan Kaufmann.
9. Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32(1), 63-76.
10. Friedman, J. (1997). On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1, 55-77.
11. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2/3), 131-163.
12. Ghahramani, Z. & Jordan, M. I. (1994). Supervised learning from incomplete data via an EM approach. In Advances in neural information processing systems 6 (pp. 120-127). San Mateo, CA: Morgan Kaufmann.
13. Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11, 63-91.
14. John, G. H. & Kohavi, R. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1/2), 273-324.
15. John, G. H. & Langley P. (1995). Estimating continuous distributions in Bayesian Classifiers. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec (pp. 338-345). San Mateo, CA: Morgan Kaufmann.
16. Kasif, S., Salzberg, S., Waltz, D., Rachlin, J., & Aha, D. W (1998). A probabilistic framework for memory-based reasoning. Artificial Intelligence, 104(1/2), 297-312.
17. Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction using instance-based learning with encoding length selection. In Progress in Connectionist-Based Information Systems, Dunedin, New Zealand (pp. 984-987). Singapore: Springer-Verlag.
18. Kononenko, I. (1991). Semi-naive Bayesian classifier. In Proceedings of the 6th European Working Session on Learning, Porto, Portugal (pp. 206-219). Berlin: Springer-Verlag.
19. Kononenko, I. (1998). Personal Communication.
20. Langley, P., Iba, W., & Thompson, K. (1992). An analysis of Bayesian classifiers. In Proceedings of the 10th National Conference on Artificial Intelligence, San Jose, CA (pp. 223-228). Menlo Park, CA: AAAI Press.
21. Langley, P. (1993). Induction of recursive Bayesian classifiers. In Proceedings of the 8th European Conference on Machine Learning, Vienna, Austria (pp. 153-164). Berlin: Springer-Verlag.
22. Langley, P. & Sage, S. (1994). Induction of selective Bayesian classifiers, In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, Seattle, WA (pp. 399-406). San Mateo, CA: Morgan Kaufmann.
23. Lehmann, E. L. (1983). Theory of point estimation. New York: Wiley.
24. Pazzani, M. (1996). Searching for dependencies in Bayesian classifiers. In Learning from data: Artificial intelligence and statistics V (pp. 343-348). New York: Springer-Verlag.
25. Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia (pp. 343-348). Singapore: World Scientific.
26. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
27. Sahami, M. (1996). Learning limited dependence Bayesian classifiers. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR (pp. 335-338). Menlo Park, CA: AAAI Press.
28. Silverman, B. W. (1986). Density estimation for statistics and data analysis. New York: Chapman and Hall.
29. Simonoff, J. S. (1996). Smoothing methods in statistics. New York: Springer-Verlag.
30. Smyth, P., Gray, A., & Fayyad, U. M. (1995). Retrofitting decision tree classifiers using kernel density estimation, In Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA (pp. 506-514). San Francisco, CA: Morgan Kaufmann.
31. StatLib (1999). Department of Statistics, Carnegie Mellon University. [http://lib.stat.cmu.edu].
32. Wang, Y. & Witten, I. H. (1997). Induction of model trees for predicting continuous classes, In Proceedings of the Poster Papers of the European Conference on Machine Learning, Prague (pp. 128-137). Prague: University of Economics, Faculty of Informatics and Statistics.