Empirical Performance Assessment of Nonlinear Model Selection Techniques
BibTeX
@MISC{Elisa_empiricalperformance,
author = {Guerrero Vázquez Elisa and Pizarro Junquera Joaquín and Yáñez Escolano Andrés and Galindo Riaño Pedro L and Grupo Sistemas Inteligentes De Computación and Dpto Lenguajes Y Sistemas},
title = {Empirical Performance Assessment of Nonlinear Model Selection Techniques},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
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. 1
Keyphrases
nonlinear model selection technique empirical performance assessment empirical comparison model selection technique different model selection criterion expected error particular reference prediction risk estimation feedforward neural network prediction risk important tool large-scale small-samples simulation model selection nonlinear regularized neural network
