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A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373 Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] reconnaissance automatique Résumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250 [article]

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