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Knowledge Extraction from Unsupervised Multi-topographic Neural Network Models

Shadi Al Shehabi 1 Jean-Charles Lamirel 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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https://hal.inria.fr/inria-00000841
Contributor : Shadi Al Shehabi <>
Submitted on : Wednesday, November 30, 2005 - 11:28:23 AM
Last modification on : Friday, February 26, 2021 - 3:28:03 PM
Long-term archiving on: : Thursday, September 23, 2010 - 3:11:55 PM

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Shadi Al Shehabi, Jean-Charles Lamirel. Knowledge Extraction from Unsupervised Multi-topographic Neural Network Models. International Conference on Artificial Neural Networks - ICANN 2005, Sep 2005, Warsaw/Poland, pp.479--484, ⟨10.1007/11550907_75⟩. ⟨inria-00000841v3⟩

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