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Conference papers

A pruned higher-order network for knowledge extraction

Laurent Bougrain 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Usually, the learning stage of a neural network leads to a single model. But a complex problem cannot always be solved adequately by a global system. On the other side, several systems specialized on a subspace have some difficulties to deal with situations located at the limit of two classes. This article presents a new adaptive architecture based upon higher-order computation to adjust a general model to each pattern and using a pruning algorithm to improve the generalization and extract knowledge. We use one small multi-layer perceptron to predict each weight of the model from the current pattern (we have one estimator per weight). This architecture introduces a higher-order computation, biologically inspired, similar to the modulation of a synapse between two neurons by a third neuron. The general model can then be smaller, more adaptative and more informative.
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Submitted on : Tuesday, September 26, 2006 - 9:06:32 AM
Last modification on : Friday, February 4, 2022 - 3:18:13 AM


  • HAL Id : inria-00099439, version 1



Laurent Bougrain. A pruned higher-order network for knowledge extraction. International Joint Conference on Neural Networks - IJCNN'02, May 2002, Honolulu, Hawaii, USA, 4 p. ⟨inria-00099439⟩



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