InforLorV4, Main, Merge, bibRecord, 006611

Neural network topology optimization

Identifieur interne : 006611 ( Main/Merge ); précédent : 006610; suivant : 006612

Neural network topology optimization

Auteurs : Mohammed Attik [France] ; Laurent Bougrain [France] ; Frédéric Alexandre [France]

Source :

Lecture notes in computer science [ 0302-9743 ] ; 2005.

RBID : Pascal:06-0067795

Descripteurs français

Pascal (Inist)
- Méthode formelle, Haute performance, Intelligence artificielle, Solution optimale, Classification, Topologie circuit, Réseau neuronal, Optimisation, Méthode optimisation, Algorithme apprentissage, ..
Wicri :
- topic : Intelligence artificielle, Classification.

English descriptors

KwdEn :
- Artificial intelligence, Classification, Formal method, High performance, Learning algorithm, Network topology, Neural network, Optimal solution, Optimization, Optimization method.

Abstract

The determination of the optimal architecture of a supervised neural network is an important and a difficult task. The classical neural network topology optimization methods select weight(s) or unit(s) from the architecture in order to give a high performance of a learning algorithm. However, all existing topology optimization methods do not guarantee to obtain the optimal solution. In this work, we propose a hybrid approach which combines variable selection method and classical optimization method in order to improve optimization topology solution. The proposed approach suggests to identify the relevant subset of variables which gives a good classification performance in the first step and then to apply a classical topology optimization method to eliminate unnecessary hidden units or weights. A comparison of our approach to classical techniques for architecture optimization is given.

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Pascal:06-0067795

Le document en format XML

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<front><div type="abstract" xml:lang="en">The determination of the optimal architecture of a supervised neural network is an important and a difficult task. The classical neural network topology optimization methods select weight(s) or unit(s) from the architecture in order to give a high performance of a learning algorithm. However, all existing topology optimization methods do not guarantee to obtain the optimal solution. In this work, we propose a hybrid approach which combines variable selection method and classical optimization method in order to improve optimization topology solution. The proposed approach suggests to identify the relevant subset of variables which gives a good classification performance in the first step and then to apply a classical topology optimization method to eliminate unnecessary hidden units or weights. A comparison of our approach to classical techniques for architecture optimization is given.</div>
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Neural network topology optimization

Neural network topology optimization

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri