InforLorV4, PascalFrancis, Corpus, bibRecord, 000B99

Scope classification : An instance-based learning algorithm with a rule-based characterisation

Identifieur interne : 000B99 ( PascalFrancis/Corpus ); précédent : 000B98; suivant : 000C00

Scope classification : An instance-based learning algorithm with a rule-based characterisation

Auteurs : N. Lachichel ; P. Marquis

Source :

Lecture notes in computer science [ 0302-9743 ] ; 1998.

RBID : Pascal:98-0263976

Descripteurs français

Pascal (Inist)
- Intelligence artificielle, Algorithme apprentissage, Complexité algorithme, Classification.

English descriptors

KwdEn :
- Algorithm complexity, Artificial intelligence, Classification, Learning algorithm.

Abstract

Scope classification is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classification of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classifiers, scope classification relies on partial pre-orderings ≤_o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

A01	`01`	`1`		`@0 0302-9743`
A05				`@2 1398`
A08	`01`	`1`	`ENG`	`@1 Scope classification : An instance-based learning algorithm with a rule-based characterisation`
A09	`01`	`1`	`ENG`	`@1 Machine learning : Chemnitz, April 21-23, 1998`
A11	`01`	`1`		`@1 LACHICHEL (N.)`
A11	`02`	`1`		`@1 MARQUIS (P.)`
A12	`01`	`1`		`@1 NEDELLEC (Claire) @9 ed.`
A12	`02`	`1`		`@1 ROUVEIROL (Céline) @9 ed.`
A14	`01`			`@1 LORIA, B.P. 239 @2 54506 Vandoeuvre-lès-Nancy @3 FRA @Z 1 aut.`
A14	`02`			`@1 CRIL/Université d'Artois, Rue de l'Université, S.P. 16 @2 62307 Lens @3 FRA @Z 2 aut.`
A20				`@1 268-279`
A21				`@1 1998`
A23	`01`			`@0 ENG`
A26	`01`			`@0 3-540-64417-2`
A43	`01`			`@1 INIST @2 16343 @5 354000078743660330`
A44				`@0 0000 @1 © 1998 INIST-CNRS. All rights reserved.`
A45				`@0 13 ref.`
A47	`01`	`1`		`@0 98-0263976`
A60				`@1 P @2 C`
A61				`@0 A`
A64		`1`		`@0 Lecture notes in computer science`
A66	`01`			`@0 DEU`
A66	`02`			`@0 USA`
C01	`01`		`ENG`	@0 Scope classification is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classification of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classifiers, scope classification relies on partial pre-orderings ≤_o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.
C02	`01`	`X`		`@0 001D02C02`
C03	`01`	`X`	`FRE`	`@0 Intelligence artificielle @5 01`
C03	`01`	`X`	`ENG`	`@0 Artificial intelligence @5 01`
C03	`01`	`X`	`SPA`	`@0 Inteligencia artificial @5 01`
C03	`02`	`X`	`FRE`	`@0 Algorithme apprentissage @5 02`
C03	`02`	`X`	`ENG`	`@0 Learning algorithm @5 02`
C03	`02`	`X`	`SPA`	`@0 Algoritmo aprendizaje @5 02`
C03	`03`	`X`	`FRE`	`@0 Complexité algorithme @5 03`
C03	`03`	`X`	`ENG`	`@0 Algorithm complexity @5 03`
C03	`03`	`X`	`SPA`	`@0 Complejidad algoritmo @5 03`
C03	`04`	`X`	`FRE`	`@0 Classification @5 04`
C03	`04`	`X`	`ENG`	`@0 Classification @5 04`
C03	`04`	`X`	`GER`	`@0 Klassifizierung @5 04`
C03	`04`	`X`	`SPA`	`@0 Clasificación @5 04`
N21				`@1 173`

A30	`01`	`1`	`ENG`	`@1 ECML-98 : European conference on machine learning @2 10 @3 Chemnitz DEU @4 1998-04-21`

Format Inist (serveur)

NO :	PASCAL 98-0263976 INIST
ET :	Scope classification : An instance-based learning algorithm with a rule-based characterisation
AU :	LACHICHEL (N.); MARQUIS (P.); NEDELLEC (Claire); ROUVEIROL (Céline)
AF :	LORIA, B.P. 239/54506 Vandoeuvre-lès-Nancy /France (1 aut.); CRIL/Université d'Artois, Rue de l'Université, S.P. 16/62307 Lens/France (2 aut.)
DT :	Publication en série; Congrès; Niveau analytique
SO :	Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 1998; Vol. 1398; Pp. 268-279; Bibl. 13 ref.
LA :	Anglais
EA :	Scope classification is a new instance-based learning (IBL) technique with a rule-based characterisation. Within the scope approach, the classification of an object o is based on the examples that are closer to o than every example labelled with another class. In contrast to standard distance-based IBL classifiers, scope classification relies on partial pre-orderings ≤_o between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.
CC :	001D02C02
FD :	Intelligence artificielle; Algorithme apprentissage; Complexité algorithme; Classification
ED :	Artificial intelligence; Learning algorithm; Algorithm complexity; Classification
GD :	Klassifizierung
SD :	Inteligencia artificial; Algoritmo aprendizaje; Complejidad algoritmo; Clasificación
LO :	INIST-16343.354000078743660330
ID :	98-0263976

Links to Exploration step

Pascal:98-0263976

Le document en format XML

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 between examples, indexed by objects. Interestingly, the notion of closeness to o that is used characterises the classes predicted by all the rules that cover o and are relevant and consistent for the training set. Accordingly, scope classification is an IBL technique with a rule-based characterisation. Since rules do not have to be explicitly generated, the scope approach applies to classification problems where the number of rules prevents them from being exhaustively computed.</EA>
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Scope classification : An instance-based learning algorithm with a rule-based characterisation

Scope classification : An instance-based learning algorithm with a rule-based characterisation

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