Abstract
Estimating Prediction Risk is important for providing a way of computing the expected error for predictions made by a model, but it is also an important tool for model selection. This paper addresses an empirical comparison of model selection techniques based on the Prediction Risk estimation, with particular reference to the structure of nonlinear regularized neural networks. To measure the performance of the different model selection criteria a large-scale small-samples simulation is conducted for feedforward neural networks.
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References
Bishop C. M.: Neural networks for pattern recognition. Clarendon Press, Oxford (1995)
Brake, G., Kok J.N. Vitányi P.M.B.: Model Selection for Neural Networks: Comparing MDL and NIC. In: Proc. European Symposium on Artificial Neural Networks, Brussels, April 20-22 (1994)
Larsen J., Hansen L.K.: Generalization performance of regularized neural network models. Proc. IEEE Workshop: Neural Networks for Signal Processing IV, Piscataway, New Jersey (1994) 42–51
Lawrence S., Giles C.L., Tsoi A.C.: What Size of Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation. Technical Report. UMIACS-TR-96-22 and CS-TR-3617. Institute of Advanced Computer Studies. University of Mariland. (1996)
McQuarrie A., Tsai C.: Regression and Time Series Model Selection. World Scientific Publishing Co. Pte. Ltd. (1998)
Moody, J.: The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. NIPS (1992) 847–854
Moody, J.: Prediction Risk and Architecture Selection for Neural Networks. In Cherkassky, V., Friedman, J. H., and Wechsler, H., editors, From Statistics to Neural Networks: Theory and Pattern Recognition Applications, NATO ASI Series F. Springer-Verlag (1994)
Murata N., Yoshizawa S., Amari S.: Network Information Criterion-Determining the Number of Hidden Units for an Artificial Neural Network Model. IEEE Transactions on Neural Networks (1994) 5, 865–872
Ripley B.D. Statistical Ideas for Selecting Network Architectures. Neural Networks: Artificial Intelligence & Industrial Applications, eds. B. Kappend and S. Gielen. Springer, Berlin (1995) 183–190
Sarle W.: Donojo-Jonhstone benchmarks: neural nets results (1999) ftp://ftp.sas.com/ pub/neural/dojo/dojo.html
Zapranis A., Refenes A.: Principles of Neural Model Identification, Selection and Adequacy: with applications to financial economics. (Perspectives in neural computing). Springer-Verlag London (1999)
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Guerrero Vázquez, E., Pizarro Junquera, J., Yáñez Escolano, A., Riaño Galindo, P.L. (2002). Empirical Performance Assessment of Nonlinear Model Selection Techniques. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_46
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DOI: https://doi.org/10.1007/3-540-36131-6_46
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