Graphes prototypes vs. graphe médian généralisé pour la classification de données structurées
Identifieur interne : 000D09 ( Main/Exploration ); précédent : 000D08; suivant : 000D10Graphes prototypes vs. graphe médian généralisé pour la classification de données structurées
Auteurs : Romain Raveaux [France] ; Eugen Barbu [France] ; Sébastien Adam [France] ; Pierre Héroux [France] ; Éric Trupin [France]Source :
Descripteurs français
- mix :
Abstract
Dans [BAR 06], nous avons présenté une méthode de classification de données structurelles basée sur l'utilisation de graphes prototypes extraits par un algorithme génétique. Dans ce nouvel article, nous comparons d'un point de vue théorique et d'un point de vue expérimental cette approche à l'utilisation des graphes médians généralisés. Nous montrons que la modification du critère de choix des prototypes pour tenir compte de la distribution inter-classe des données, ainsi que la possibilité d'extraire plusieurs représentants par classe permettent d'améliorer de façon significative les performances de classification.
Url:
Affiliations:
- France
- Haute-Normandie, Poitou-Charentes, Région Normandie
- La Rochelle, Le Havre, Rouen
- Université de La Rochelle, Université de Rouen, Université du Havre
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000543
- to stream Hal, to step Curation: 000543
- to stream Hal, to step Checkpoint: 000575
- to stream Main, to step Merge: 000D23
- to stream Main, to step Curation: 000D09
Le document en format XML
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type="university">Université du Havre</orgName><placeName><settlement type="city">Rouen</settlement><region type="region" nuts="2">Région Normandie</region><region type="old region" nuts="2">Haute-Normandie</region></placeName><orgName type="university">Université de Rouen</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>Classification supervisée de graphes</term><term>Graphes prototypes</term><term>algorithmes génétiques</term><term>algorithmes génétiques.</term><term>graphes médians généralisés</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Dans [BAR 06], nous avons présenté une méthode de classification de données structurelles basée sur l'utilisation de graphes prototypes extraits par un algorithme génétique. Dans ce nouvel article, nous comparons d'un point de vue théorique et d'un point de vue expérimental cette approche à l'utilisation des graphes médians généralisés. Nous montrons que la modification du critère de choix des prototypes pour tenir compte de la distribution inter-classe des données, ainsi que la possibilité d'extraire plusieurs représentants par classe permettent d'améliorer de façon significative les performances de classification.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Poitou-Charentes</li><li>Région Normandie</li></region><settlement><li>La Rochelle</li><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de La Rochelle</li><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Poitou-Charentes"><name sortKey="Raveaux, Romain" sort="Raveaux, Romain" uniqKey="Raveaux R" first="Romain" last="Raveaux">Romain Raveaux</name></region><name sortKey="Adam, Sebastien" sort="Adam, Sebastien" uniqKey="Adam S" first="Sébastien" last="Adam">Sébastien Adam</name><name sortKey="Barbu, Eugen" sort="Barbu, Eugen" uniqKey="Barbu E" first="Eugen" last="Barbu">Eugen Barbu</name><name sortKey="Heroux, Pierre" sort="Heroux, Pierre" uniqKey="Heroux P" first="Pierre" last="Héroux">Pierre Héroux</name><name sortKey="Trupin, Eric" sort="Trupin, Eric" uniqKey="Trupin E" first="Éric" last="Trupin">Éric Trupin</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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