A pruned higher-order network for knowledge extraction
Identifieur interne :
000726 ( PascalFrancis/Corpus );
précédent :
000725;
suivant :
000727
A pruned higher-order network for knowledge extraction
Auteurs : Laurent BougrainSource :
-
IEEE ... International Conference on Neural Networks [ 1098-7576 ] ; 2002.
RBID : Pascal:04-0132540
Descripteurs français
English descriptors
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
| pA |
| A01 | 01 | 1 | | @0 1098-7576 |
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| A08 | 01 | 1 | ENG | @1 A pruned higher-order network for knowledge extraction |
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| A09 | 01 | 1 | ENG | @1 IJCNN'02 : international joint conference on neural networks : Honolulu HI, 12-17 May 2002 |
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| A11 | 01 | 1 | | @1 BOUGRAIN (Laurent) |
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| A14 | 01 | | | @1 LORIA INRIA-Lorraine, B.P. 239 @2 54506 Vandoeuvre-Les-Nancy @3 FRA @Z 1 aut. |
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| A18 | 01 | 1 | | @1 IEEE. Neural Networks Society @3 USA @9 patr. |
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| A18 | 02 | 1 | | @1 International Neural Network Society @3 USA @9 patr. |
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| A20 | | | | @1 1726-1729 |
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| A21 | | | | @1 2002 |
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| A23 | 01 | | | @0 ENG |
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| A26 | 01 | | | @0 0-7803-7278-6 |
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| A43 | 01 | | | @1 INIST @2 Y 37961 @5 354000117750883070 |
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| A44 | | | | @0 0000 @1 © 2004 INIST-CNRS. All rights reserved. |
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| A45 | | | | @0 12 ref. |
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| A47 | 01 | 1 | | @0 04-0132540 |
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| A60 | | | | @1 P @2 C |
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| A61 | | | | @0 A |
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| A64 | 01 | 1 | | @0 IEEE ... International Conference on Neural Networks |
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| A66 | 01 | | | @0 USA |
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| C01 | 01 | | ENG | @0 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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| C02 | 01 | X | | @0 001D02C06 |
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| C03 | 01 | X | FRE | @0 Réseau multicouche @5 01 |
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| C03 | 01 | X | ENG | @0 Multilayer network @5 01 |
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| C03 | 01 | X | SPA | @0 Red multinivel @5 01 |
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| C03 | 02 | 3 | FRE | @0 Perceptron multicouche @5 02 |
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| C03 | 02 | 3 | ENG | @0 Multilayer perceptrons @5 02 |
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| C03 | 03 | X | FRE | @0 Réseau neuronal @5 03 |
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| C03 | 03 | X | ENG | @0 Neural network @5 03 |
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| C03 | 03 | X | SPA | @0 Red neuronal @5 03 |
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| C03 | 04 | X | FRE | @0 Synapse @5 04 |
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| C03 | 04 | X | ENG | @0 Synapse @5 04 |
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| C03 | 04 | X | SPA | @0 Sinapsis @5 04 |
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| C03 | 05 | X | FRE | @0 Mode ordre élevé @5 05 |
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| C03 | 05 | X | ENG | @0 High order mode @5 05 |
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| C03 | 05 | X | SPA | @0 Modo orden elevado @5 05 |
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| C03 | 06 | X | FRE | @0 Méthode adaptative @5 06 |
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| C03 | 06 | X | ENG | @0 Adaptive method @5 06 |
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| C03 | 06 | X | SPA | @0 Método adaptativo @5 06 |
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| C03 | 07 | X | FRE | @0 Extraction connaissance @4 CD @5 96 |
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| C03 | 07 | X | ENG | @0 Knowledge extraction @4 CD @5 96 |
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| N21 | | | | @1 082 |
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| N82 | | | | @1 PSI |
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| pR |
| A30 | 01 | 1 | ENG | @1 2002 International joint conference on neural networks @3 Honolulu HI USA @4 2002-05-12 |
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Format Inist (serveur)
| NO : | PASCAL 04-0132540 INIST |
|---|
| ET : | A pruned higher-order network for knowledge extraction |
|---|
| AU : | BOUGRAIN (Laurent) |
|---|
| AF : | LORIA INRIA-Lorraine, B.P. 239/54506 Vandoeuvre-Les-Nancy/France (1 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | IEEE ... International Conference on Neural Networks; ISSN 1098-7576; Etats-Unis; Da. 2002; Pp. 1726-1729; Bibl. 12 ref. |
|---|
| LA : | Anglais |
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| EA : | 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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| CC : | 001D02C06 |
|---|
| FD : | Réseau multicouche; Perceptron multicouche; Réseau neuronal; Synapse; Mode ordre élevé; Méthode adaptative; Extraction connaissance |
|---|
| ED : | Multilayer network; Multilayer perceptrons; Neural network; Synapse; High order mode; Adaptive method; Knowledge extraction |
|---|
| SD : | Red multinivel; Red neuronal; Sinapsis; Modo orden elevado; Método adaptativo |
|---|
| LO : | INIST-Y 37961.354000117750883070 |
|---|
| ID : | 04-0132540 |
|---|
Links to Exploration step
Pascal:04-0132540
Le document en format XML
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