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- W633285750 abstract "This paper concerns the classification of a scene observed by different types of images, that generates large amounts of data to be processed. We have chosen to use the classification SVM (Support Vector Machine) which is efficient to deal with large data. Although different sources of information can provide complementary information to lift up ambiguities, at the same time they introduce some redundant information. Our idea to fuse these data is to extract the useful information from these data to obtain an effective classification. The selection of which features are most discriminative is carried out in the kernel space of SVM, because the selection can be done linearly in this space. This also helps to reduce the size of data to get better a classification. The selection criteria are based on the separability of classes. We propose a system based on SVM classification with the selection of characteristics to classify a brain tumor from three types of 3D MRI images. Our system can follow up the evolution of a tumor during a therapeutic treatment." @default.
- W633285750 created "2016-06-24" @default.
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- W633285750 date "2009-01-01" @default.
- W633285750 modified "2023-09-23" @default.
- W633285750 title "Fusion et classification d'images multi-sources par SVM avec sélection des caractéristiques dans l'espace à noyau" @default.
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