Abstract
This paper compares several model selection methods, based on experimental estimates of their generalization errors. Experiments in the context of nonlinear time series prediction by Radial-Basis Function Networks show the superiority of the bootstrap methodology over classical cross-validations and leave-one-out.
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References
Ljung, L.: System Identification-Theory for the user, 2nd ed, Prentice Hall, 1999.
Efron, B., Tibshirani, R. J. An introduction to the bootstrap, Chapman & Hall, 1993.
Efron, B, Tibshirani, R.: Improvements on cross-validation: The.632+ bootstrap method. J. Amer. Statist. Assoc. (1997) 92:548–560.
Stone, M.: An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion, J. Royal. Statist. Soc., B39, 44–7, 1977.
Kohavi, R.: A study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Proc. of the 14th Int. Joint Conf. on A.I., Vol. 2, Canada, 1995.
Efron, B.: Estimating the error rate of a prediction rule: improvements on cross-validation. Journal of American Statistical Association, (1983) 78(382):316–331.
Shao, J., Tu, D.: The Jackknife and Bootstrap, Springer Series in Statistics, Springer-Verlag, New York (1995).
Weigend, A. S., Gershenfeld, N. A.: Times Series Prediction: Forecasting the future and Understanding the Past, Addison-Wesley Publishing Company (1994).
Simon, G., Lendasse, A., Wertz, V., Verleysen, M.: Fast approximation of the bootstrap for model selection, submitted to ESANN03, Bruges (Belgium), April 2003.
Benoudjit, N., Archambeau, C., Lendasse, A., Lee, J., Verleysen, M.: Width optimization of the Gaussian kernels in Radial Basis Function Networks, ESANN 2002, European Symposium on Artificial Neural Networks, Bruges (2002) 425–432.
Moody, J., Darken, C.: Learning with localised receptive fields, Proceedings of the 1988 Connectionist Models Summer School, San Mateo, CA (1989).
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Lendasse, A., Wertz, V., Verleysen, M. (2003). Model Selection with Cross-Validations and Bootstraps — Application to Time Series Prediction with RBFN Models. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_68
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DOI: https://doi.org/10.1007/3-540-44989-2_68
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