A parameter-free classification method for large scale learning

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 1367-1385

  Boullé, Marc ^a  

^a ORANGE LABS   (France)

Author keywords

Bayesianism; Large scale learning; Model averaging; Model selection; Naive bayes

Indexed keywords

BAYESIANISM; LARGE SCALE LEARNING; MODEL AVERAGING; MODEL SELECTION; NAIVE BAYES;

MINING;

EDUCATION;

EID: 68949146038     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. A. Bordes and L. Bottou. Sgd-qn, larank: Fast optimizers for linear svms. In ICML 2008 Workshop for PASCAL Large Scale Learning Challenge, 2008. http://largescale.first.fraunhofer.de/workshop/.
2. M. Boullé;;. A Bayes optimal approach for partitioning the values of categorical attributes. Journal of Machine Learning Research, 6:1431-1452, 2005.
3. M. Boullé;;. MODL: a Bayes optimal discretization method for continuous attributes. Machine Learning, 65(1):131-165, 2006.
4. M. Boullé;;. Compression-based averaging of selective naive Bayes classifiers. Journal of Machine Learning Research, 8:1659-1685, 2007.
5. M. Boullé;;. An efficient parameter-free method for large scale offline learning. In ICML 2008 Workshop for PASCAL Large Scale Learning Challenge, 2008. http://largescale.first.fraunhofer.de/workshop/.
6. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
7. O. Chapelle. Training a support vector machine in the primal. Neural Computation, 19:1155-1178, 2007.
8. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth. CRISP-DM 1.0 : Step-by-step Data Mining Guide, 2000.
9. J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In Proceedings of the 12th International Conference on Machine Learning, pages 194-202. Morgan Kaufmann, San Francisco, CA, 1995.
10. T. Fawcett. ROC graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories, 2003.
11. U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. Knowledge discovery and data mining: towards a unifying framework. In KDD, pages 82-88, 1996.
12. D.J. Hand and K. Yu. Idiot bayes ? not so stupid after all? International Statistical Review, 69(3): 385-399, 2001.
13. J.A. Hoeting, D. Madigan, A.E. Raftery, and C.T. Volinsky. Bayesian model averaging: A tutorial. Statistical Science, 14(4):382-417, 1999.
14. C-J. Hsieh, K-W. Chang, C-J. Lin, S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear svm. In ICML 08: Proceedings of the 25th international conference on Machine learning, pages 408-415, New York, NY, USA, 2008. ACM.
15. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In European Conference on Machine Learning (ECML), pages 137-142, Berlin, 1998. Springer.
16. R. Kohavi and G. John. Wrappers for feature selection. Artificial Intelligence, 97(1-2):273-324, 1997.
17. P. Langley and S. Sage. Induction of selective Bayesian classifiers. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, pages 399-406. Morgan Kaufmann, 1994.
18. P. Langley, W. Iba, and K. Thompson. An analysis of Bayesian classifiers. In 10th National Conference on Artificial Intelligence, pages 223-228. AAAI Press, 1992.
19. H. Liu, F. Hussain, C.L. Tan, and M. Dash. Discretization: An enabling technique. Data Mining and Knowledge Discovery, 4(6):393-423, 2002.
20. R. Mamdouh. Data Preparation for Data Mining Using SAS. Morgan Kaufmann Publishers, 2006.
21. D. Pyle. Data Preparation for Data Mining. Morgan Kaufmann Publishers, Inc. San Francisco, USA, 1999.
22. S. Sonnenburg, V. Franc, E. Yom-Tov, and M. Sebag. Pascal large scale learning challenge, 2008. http://largescale.first.fraunhofer.de/about/.
23. S. Vempala. The Random ProjectionMethod, volume 65 of DIMACS Series in DiscreteMathematics and Theoretical Computer Science. American Mathematical Society, 2004.