By Topic

Two original weight pruning methods based on statistical tests and rounding techniques

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 $31
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

2 Author(s)
Ledoux, C. ; INRETS-MAIA, Arcueil, France ; Grandin, J.F.

The authors focus on the use of neural networks to approximate continuous decision functions. In this context, the parameters to be estimated are the synaptic weights of the network. The number of such parameters and the quantity of data (information) available for training greatly influence the quality of the solution obtained. A previous study analysed the influence and interaction of these two features. In order to reach the architecture of the net leading to the best fitting of the training data, two original pruning techniques are proposed. The evolution of the neural network performances, training and test rates, as the number of synaptic weights pruned increases, is shown experimentally. Two kinds of synaptic weights are obvious: irrelevant synaptic weights, which can be suppressed from the model; and relevant synaptic weights, which cannot be removed. In the test problem, it is possible to reduce the size of the network up to 42%. A 4% improvement of the performance in generalisation is observed

Published in:

Vision, Image and Signal Processing, IEE Proceedings -  (Volume:141 ,  Issue: 4 )