  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.
