Mathematics – Statistics Theory
Scientific paper
2007-05-02
Comptes Rendus de l Acad\'emie des Sciences - Series I - Mathematics 343, 8 (15/10/2006) 555-560
Mathematics
Statistics Theory
6 pages
Scientific paper
10.1016/j.crma.2006.09.025
This Note proposes a new methodology for function classification with Support Vector Machine (SVM). Rather than relying on projection on a truncated Hilbert basis as in our previous work, we use an implicit spline interpolation that allows us to compute SVM on the derivatives of the studied functions. To that end, we propose a kernel defined directly on the discretizations of the observed functions. We show that this method is universally consistent.
Rossi Fabrice
Villa Nathalie
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