InforLorV4, PascalFrancis, Corpus, bibRecord, 000C05

Knowledge extraction from neural networks for signal interpretation

Identifieur interne : 000C05 ( PascalFrancis/Corpus ); précédent : 000C04; suivant : 000C06

Knowledge extraction from neural networks for signal interpretation

Auteurs : F. Alexandre ; J.-F. Remm

Source :

RBID : Pascal:98-0230852

Descripteurs français

Pascal (Inist)
- Radar, Réseau neuronal, Théorie signal, Interprétation information, Cible, Perceptron multicouche, Extraction connaissance.

English descriptors

KwdEn :
- Information interpretation, Knowledge extraction, Multilayer perceptron, Neural network, Radar, Signal theory, Target.

Abstract

Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A08	`01`	`1`	`ENG`	`@1 Knowledge extraction from neural networks for signal interpretation`
A09	`01`	`1`	`ENG`	`@1 ESANN '97 : European symposium on artificial neural networks : Bruges, 16-18 April 1997`
A11	`01`	`1`		`@1 ALEXANDRE (F.)`
A11	`02`	`1`		`@1 REMM (J.-F.)`
A12	`01`	`1`		`@1 VERLEYSEN (Michel) @9 ed.`
A14	`01`			`@1 CRIN-INRIA, BP 239 @2 54506 Vandoeuvre @3 FRA @Z 1 aut. @Z 2 aut.`
A20				`@1 115-120`
A21				`@1 1997`
A23	`01`			`@0 ENG`
A25	`01`			`@1 D Facto @2 Brussels`
A26	`01`			`@0 2-9600049-7-3`
A30	`01`	`1`	`ENG`	`@1 European symposium on artificial neural networks @2 5 @3 Bruges BEL @4 1997-04-16`
A43	`01`			`@1 INIST @2 Y 31662 @5 354000068109400190`
A44				`@0 0000 @1 © 1998 INIST-CNRS. All rights reserved.`
A45				`@0 7 ref.`
A47	`01`	`1`		`@0 98-0230852`
A60				`@1 C`
A61				`@0 A`
A66	`01`			`@0 BEL`
C01	`01`		`ENG`	@0 Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.
C02	`01`	`X`		`@0 001D04B05`
C02	`02`	`X`		`@0 001D02C06`
C03	`01`	`X`	`FRE`	`@0 Radar @5 01`
C03	`01`	`X`	`ENG`	`@0 Radar @5 01`
C03	`01`	`X`	`SPA`	`@0 Radar @5 01`
C03	`02`	`X`	`FRE`	`@0 Réseau neuronal @5 02`
C03	`02`	`X`	`ENG`	`@0 Neural network @5 02`
C03	`02`	`X`	`SPA`	`@0 Red neuronal @5 02`
C03	`03`	`X`	`FRE`	`@0 Théorie signal @5 03`
C03	`03`	`X`	`ENG`	`@0 Signal theory @5 03`
C03	`03`	`X`	`SPA`	`@0 Teoría señal @5 03`
C03	`04`	`X`	`FRE`	`@0 Interprétation information @5 04`
C03	`04`	`X`	`ENG`	`@0 Information interpretation @5 04`
C03	`04`	`X`	`SPA`	`@0 Interpretación información @5 04`
C03	`05`	`X`	`FRE`	`@0 Cible @5 05`
C03	`05`	`X`	`ENG`	`@0 Target @5 05`
C03	`05`	`X`	`SPA`	`@0 Blanco @5 05`
C03	`06`	`X`	`FRE`	`@0 Perceptron multicouche @4 CD @5 96`
C03	`06`	`X`	`ENG`	`@0 Multilayer perceptron @4 CD @5 96`
C03	`07`	`X`	`FRE`	`@0 Extraction connaissance @4 CD @5 97`
C03	`07`	`X`	`ENG`	`@0 Knowledge extraction @4 CD @5 97`
N21				`@1 153`

Format Inist (serveur)

NO :	PASCAL 98-0230852 INIST
ET :	Knowledge extraction from neural networks for signal interpretation
AU :	ALEXANDRE (F.); REMM (J.-F.); VERLEYSEN (Michel)
AF :	CRIN-INRIA, BP 239 /54506 Vandoeuvre /France (1 aut., 2 aut.)
DT :	Congrès; Niveau analytique
SO :	European symposium on artificial neural networks/5/1997-04-16/Bruges BEL; Belgique; Brussels: D Facto; Da. 1997; Pp. 115-120; ISBN 2-9600049-7-3
LA :	Anglais
EA :	Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.
CC :	001D04B05; 001D02C06
FD :	Radar; Réseau neuronal; Théorie signal; Interprétation information; Cible; Perceptron multicouche; Extraction connaissance
ED :	Radar; Neural network; Signal theory; Information interpretation; Target; Multilayer perceptron; Knowledge extraction
SD :	Radar; Red neuronal; Teoría señal; Interpretación información; Blanco
LO :	INIST-Y 31662.354000068109400190
ID :	98-0230852

Links to Exploration step

Pascal:98-0230852

Le document en format XML

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<front><div type="abstract" xml:lang="en">Artificial neural networks have proved their ability to perform classification tasks. This ability is not satisfactory when expertise of the application domain is not available or when experts want to know more about hints that led to the decision. This leads presently to a great amount of work for knowledge or rule extraction from neural networks. In this paper, we propose a technique able to extract rules and to explain the functioning of the hidden layers of a multilayer perceptron. The first step consists in pruning the network with the classical OBD algorithm. Then, tightening of the sigmoidal transfer function can simply result in such knowledge extraction. This principle has been first tested on an application of signal interpretation in the radar domain.</div>
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Knowledge extraction from neural networks for signal interpretation

Knowledge extraction from neural networks for signal interpretation

Source :

Descripteurs français

English descriptors

Abstract

Notice en format standard (ISO 2709)

Format Inist (serveur)

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