Iterative Bayes

Theoretical Computer Science

Volumn 292, Issue 2, 2003, Pages 417-430

  Gama, João ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Iterative optimization; Naive Bayes; Supervised machine learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); ITERATIVE METHODS; LEARNING ALGORITHMS; OPTIMIZATION; PATTERN RECOGNITION; PROBABILITY; STATISTICS; THEOREM PROVING;

DATA SETS;

LEARNING SYSTEMS;

EID: 0037467655     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(02)00179-2     Document Type: Conference Paper

References (23)

1. C. Blake, E. Keogh, C. Merz, UCI repository of Machine Learning databases, 1999, URL http://www.ics.uci.edu/mlearn/MLRepository.html.
2. Brodley C.E., Utgoff P.E. Multivariate decision trees. Mach. Learning. 19:1995;45-77.
3. Domingos P., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Mach. Learning. 29:1997;103-129.
4. Dougherty J., Kohavi K., Sahami M. Supervised and unsupervised discretization of continuous features. Prieditis A., Russel S. Proc. 12th Internat. Conf. on Machine Learning. 1995;Morgan Kaufmann, Los Altos, CA.
5. Duda R., Hart P. Pattern Classification and Scene Analysis. 1973;Wiley, New York.
6. C. Elkan, Boosting and naive Bayesian learning, Technical Report CS97-557, University of California, San Diego, 1997.
7. Gama J. A Linear-Bayes classifier. Monard C. Advances on Artificial Intelligence-SBIA2000. Lecture Notes in Artificial Intelligence. Vol. 1952:2000;Springer, Berlin.
8. G. John, Enhancements to the data mining process, Ph.D. Thesis, Stanford University, 1997.
9. John G., Kohavi R., Pfleger K. Irrelevant features and the subset selection problem. Cohen W., Hirsh H. Proc. 11th Internat. Conf. on Machine Learning. 1994;Morgan Kaufmann, Los Altos, CA.
10. R. Kohavi, Scaling up the accuracy of naive Bayes classifiers: a decision tree hybrid, in: Proc. 2nd Internat. Conf. on Knowledge Discovery and Data Mining, AAAI Press, New York, 1996.
11. Kohavi R., Wolpert D.H. Bias plus variance decomposition for zero-one loss functions. Saitta L. Proc. 13th Internat. Conf. on Machine Learning. 1996;Morgan Kaufmann, Los Altos, CA.
12. Kononenko I. Semi-naive Bayesian classifier. Kodratoff Y. European Working Session on Learning-EWSL91. Lecture Notes in Artificial Intelligence. Vol. 482:1991;Springer, Berlin.
13. Langley P. Induction of recursive Bayesian classifiers. Brazdil P. Proc. European Conf. on Machine Learning. Lecture Notes in Artificial Intelligence. Vol. 667:1993;Springer, Berlin.
14. Langley P. Tractable average-case analysis of naive Bayesian classifiers. Bratko I., Dzeroski S. Proc. 16th Internat. Conf. on Machine Learning. 1999;Morgan Kaufmann, Los Altos, CA.
15. Liu H., Motoda H. Feature Selection for Knowledge Discovery and Data Mining. 1998;Kluwer Academic Publishers, Dodrecht.
16. Michie D., Spiegelhalter D., Taylor C. Machine Learning, Neural and Statistical Classification. 1994;Ellis Horwood, Chichester, UK.
17. Mitchell T. Machine Learning. 1997;MacGraw-Hill, New York.
18. Nigam K., Mccallum A.K., Thrun S., Mitchell T. Text classification from labeled and unlabeled documents using EM. Mach. Learning. 39:2000;1-32.
19. M. Pazzani, Constructive induction of Cartesian product attributes, in: Proc. Conf. ISIS96: Information, Statistics and Induction in Science, World Scientific, Singapore, 1996, pp. 66-77.
20. Ridgeway G., Madigan D., Richardson T., O'Kane J. Interpretable boosted naive bayes classification. Agrawal R., Stolorz P. Proc. Fourth Internat. Conf. on Knowledge Discovery and Data Mining. 1998;AAAI Press, New York.
21. Ripley B.D. Pattern Recognition and Neural Networks. 1996;Cambridge University Press, Cambridge.
22. Schapire R., Freund Y., Bartlett P., Lee W.S. Boosting the margin: a new explanation for the effectiveness of voting methods. Fisher D. Proc. 14th Internat. Conf. on Machine Learning. 1997;Morgan Kaufmann, Los Altos, CA.
23. G. Webb, M. Pazzani, Adjusted probability naive Bayesian induction, in: Proc. 11th Australian Joint Conf. on Artificial Intelligence, World Scientific, Singapore, 1998.