Knowledge extraction from unsupervised multi-topographic neural network models
Identifieur interne : 006362 ( Main/Curation ); précédent : 006361; suivant : 006363Knowledge extraction from unsupervised multi-topographic neural network models
Auteurs : Shadi Al Shehabi [France] ; Jean-Charles Lamirel [France]Source :
- Lecture notes in computer science [ 0302-9743 ] ; 2005.
Descripteurs français
- Pascal (Inist)
- Méthode formelle, Intelligence artificielle, Découverte connaissance, Apprentissage non supervisé, Fouille donnée, Système expert, Contrôle qualité, Classification, Base donnée, Association statistique, Brevet, Propriété industrielle, Réseau neuronal, Modélisation, Base connaissance, Treillis Galois, Règle association.
- Wicri :
English descriptors
- KwdEn :
Abstract
This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.
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Pascal:06-0067861Le document en format XML
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<front><div type="abstract" xml:lang="en">This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.</div>
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