Support vector machine for functional data classification

Fabrice Rossi 1, * Nathalie Villa 2
* Auteur correspondant
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them. In fact most of the traditional data analysis tools for regression, classification and clustering have been adapted to functional inputs under the general name of functional Data Analysis (FDA). In this paper, we investigate the use of Support Vector Machines (SVMs) for functional data analysis and we focus on the problem of curves discrimination. SVMs are large margin classifier tools based on implicit non linear mappings of the considered data into high dimensional spaces thanks to kernels. We show how to define simple kernels that take into account the unctional nature of the data and lead to consistent classification. Experiments conducted on real world data emphasize the benefit of taking into account some functional aspects of the problems.
Type de document :
Article dans une revue
Neurocomputing / EEG Neurocomputing, Elsevier, 2006, 69 (7-9), pp.730-742. 〈10.1016/j.neucom.2005.12.010〉
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Contributeur : Nathalie Vialaneix <>
Soumis le : mardi 1 mai 2007 - 16:45:08
Dernière modification le : vendredi 25 mai 2018 - 12:02:04
Document(s) archivé(s) le : mardi 6 avril 2010 - 23:20:23


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Fabrice Rossi, Nathalie Villa. Support vector machine for functional data classification. Neurocomputing / EEG Neurocomputing, Elsevier, 2006, 69 (7-9), pp.730-742. 〈10.1016/j.neucom.2005.12.010〉. 〈hal-00144141〉



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