Extensions to metric-based model selection

Journal of Machine Learning Research

Volumn 3, Issue , 2003, Pages 1209-1227

  Bengio, Yoshua ^a   Chapados, Nicolas ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Metric based Methods; Model Selection

Indexed keywords

CAPACITY CONTROL; ERROR DISTRIBUTIONS; FEATURE SUBSET SELECTION; HYBRID COMBINING; METRIC-BASED METHODS; MODEL SELECTION; MODEL SELECTION METHODS; TIME-SERIES DATA;

ARTIFICIAL INTELLIGENCE;

SOFTWARE ENGINEERING;

EID: 2942731013     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Y. Bengio and D. Schuurmans. Special issue on new methods for model selection and model combination, 2002. Machine Learning, 48(1).
2. J. Y. Campbell, A.W. Lo, and A. C. MacKinlay. The Econometrics of Financial Markets. Princeton University Press, Princeton, 1997.
3. O. Chapelle, V. N. Vapnik, and Y. Bengio. Model selection for small-sample regression. Machine Learning Journal, 48(1):9-23, 2002.
4. O. Chapelle, J. Weston, L. Bottou, and V. N. Vapnik. Vicinal risk minimization. In T.K. Leen, T.G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13, pages 416-422, 2001.
5. F. X. Diebold and R. S.Mariano. Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3):253-263, 1995.
6. D. Foster and E. George. The risk inflation criterion for multiple regression. Annals of Statistics, 22:1947-1975, 1994.
7. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Machine Learning: Proceedings of Thirteenth International Conference, pages 148-156, 1996.
8. W. Newey and K. West. A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix. Econometrica, 55:703-708, 1987.
9. T. Poggio and F. Girosi. Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247:978-982, 1990.
10. J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14:1080-1100, 1986.
11. D. Schuurmans. A new metric-based approach to model selection. In Proceedings of the National Conference on Artificial Intelligence (AAAI-97), pages 552-558, 1997.
12. D. Schuurmans and F. Southey. Metric-based methods for adaptive model selection and regularization. Machine Learning, 48(1):51-84, 2002.
13. D. W. Scott. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York, 1992.
14. V. N. Vapnik. Statistical Learning Theory. Wiley, Lecture Notes in Economics and Mathematical Systems, volume 454, 1998.
15. V. N. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, Berlin, 1982.