By Topic

FPE-based criteria to dimension feedforward neural topologies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
C. Alippi ; Dipt. di Elettronica, Politecnico di Milano, Italy

This paper deals with the problem of dimensioning a feedforward neural network to learn an unknown function from input/output pairs. The ultimate goal is to tune the complexity of the neural model with the information present in the training set and to estimate its performance without needing new data for cross-validation. For generality, it is not assumed that the unknown function belongs to the family of neural models. A generalization of the final prediction error to biased models is provided, which can be applied to learn unknown functions both in noise free and noise affected applications. This is based on a new definition of the effective number of parameters used by the neural model to fit the data. New criteria for model selection are introduced and compared with the generalized prediction error and the network information criteria

Published in:

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications  (Volume:46 ,  Issue: 8 )