Comparaisons de stratégies de discrimination de masses de véhicules automobiles
Identifieur interne : 000E77 ( France/Analysis ); précédent : 000E76; suivant : 000E78Comparaisons de stratégies de discrimination de masses de véhicules automobiles
Auteurs : Alain Rakotomamonjy [France] ; Rodolphe Le Riche [France] ; David Gualandris [France] ; Stephane Canu [France]Source :
- Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle [ 0992-499X ] ; 2002.
Descripteurs français
- mix :
Abstract
L'industrie automobile utilise aujourd'hui les enquêtes en clientèle, notamment, pour le dimensionnement en fiabilité de ses composants. Le problème type du classement par masses de véhicules à partir de mesures d'accélérations et de vitesses est traité. Une méthode de discrimination optimale pour ce problème est construite en considérant 4 niveaux : choix de l'espace de recherche (sélection de variables par algorithmes évolutionnaires ou par heuristiques, analyse discriminante), choix du critère de discrimination (critère Bayésien ou de marge large), choix de la complexité et choix des paramètres des algorithmes.
Url:
DOI: 10.3166/ria.16.753-784
Affiliations:
Links toward previous steps (curation, corpus...)
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Hal:emse-00686936Le document en format XML
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Série RIA : Revue d'Intelligence Artificielle</title><idno type="ISSN">0992-499X</idno><imprint><date type="datePub">2002</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>analyse discriminante</term><term>automobile</term><term>classification</term><term>identification en dynamique</term><term>machines à support vectoriel</term><term>méthode inverse en dynamique</term><term>sélection de variables</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">L'industrie automobile utilise aujourd'hui les enquêtes en clientèle, notamment, pour le dimensionnement en fiabilité de ses composants. Le problème type du classement par masses de véhicules à partir de mesures d'accélérations et de vitesses est traité. Une méthode de discrimination optimale pour ce problème est construite en considérant 4 niveaux : choix de l'espace de recherche (sélection de variables par algorithmes évolutionnaires ou par heuristiques, analyse discriminante), choix du critère de discrimination (critère Bayésien ou de marge large), choix de la complexité et choix des paramètres des algorithmes.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Rakotomamonjy, Alain" sort="Rakotomamonjy, Alain" uniqKey="Rakotomamonjy A" first="Alain" last="Rakotomamonjy">Alain Rakotomamonjy</name></region><name sortKey="Canu, Stephane" sort="Canu, Stephane" uniqKey="Canu S" first="Stephane" last="Canu">Stephane Canu</name><name sortKey="Gualandris, David" sort="Gualandris, David" uniqKey="Gualandris D" first="David" last="Gualandris">David Gualandris</name><name sortKey="Le Riche, Rodolphe" sort="Le Riche, Rodolphe" uniqKey="Le Riche R" first="Rodolphe" last="Le Riche">Rodolphe Le Riche</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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