NDLI: Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

Content Provider	Springer Nature Link
Author	Vicen Bue, Raúl Jarabo Amores, M. Pilar Rosa Zurera, Manuel Sanz González, José L. Maldonado Bascón, Saturni
Copyright Year	2010
Abstract	To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training.
Starting Page	249
Ending Page	268
Page Count	20
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	32
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2010-11-04
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Genetic algorithms Neural networks Monte Carlo Importance sampling Mean-Square error Cross Entropy error Misclassification error Computational Intelligence Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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