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- W2290774345 abstract "and key words In recent years, many methods of analysis and classification of data based on reproducing kernel Hilbert spaces have been developed. Most of these methods incorporate the fundamental principle dictated by Vapnik et al. in Support Vector Machines, which consists in extending linear algorithms to the non-linear case by using kernels. Kernel Fisher Discriminant (KFD) is one of these nonlinear methods which provides interesting results in many practical cases. However, the use of KFD requires storage and processing of matrices whose size equals the number of available data. This may be critical when the training set is large. This paper presents a sequential KFD algorithm which does not require the manipulation of large matrices. Sequential algorithms that fulfil the same requirements as KFD are also presented to perform Kernel Principal Component Analysis (KPCA) and Kernel Generalized Discriminant Analysis (KGDA). Sequential algorithms, KPCA, KFD, Mercer kernels, SVM. traitement du signal 2004_volume 21_numero 2 97" @default.
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- W2290774345 date "2004-01-01" @default.
- W2290774345 modified "2023-10-17" @default.
- W2290774345 title "Algorithmes séquentiels pour l'analyse de données par méthodes à noyau Sequential algorithms for data analysis with kernel-based methods" @default.
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