Résumé : In this talk, I will discuss concepts and methods of
kernel-based learning for functional data. The focus is on the case
where covariates as well as responses are functions. Basic concepts of
RKHS theory are extended to the domain of functional data analysis and
the conditions under which such an extension is feasible are
discussed. Our main results demonstrate how basic properties of
kernel-based classification and regression known from multivariate
statistical analysis can be restated for functional data, if
appropriate conditions are satisfied.

Travail joint avec E. Duflos (LAGIS-EC Lille/CNRS), P. Preux
(SequeL-INRIA Lille), S. Canu (LITIS-INSA Rouen)

Cet exposé se tiendra en salle C20-13, 20ème étage, Université
Paris 1, Centre Pierre Mendès-France, 90 rue de Tolbiac, 75013 Paris
(métro : Olympiades).