Nonlinear functional regression: a functional RKHS approach

Hachem Kadri 1, 2 Emmanuel Duflos 1, 2 Philippe Preux 1, 3 Stephane Canu 4 Manuel Davy 2
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : This paper deals with functional regression, in which the input attributes as well as the response are functions. To deal with this problem, we develop a functional reproducing kernel Hilbert space approach; here, a kernel is an operator acting on a function and yielding a function. We demonstrate basic properties of these functional RKHS, as well as a representer theorem for this setting; we investigate the construction of kernels; we provide some experimental insight.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00510411
Contributor : Hachem Kadri <>
Submitted on : Wednesday, August 18, 2010 - 4:34:34 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Long-term archiving on : Friday, November 19, 2010 - 2:48:59 AM

File

HK_AISTATS2010.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00510411, version 1

Citation

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stephane Canu, Manuel Davy. Nonlinear functional regression: a functional RKHS approach. Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS'10), 2010, Italy. pp.374-380. ⟨hal-00510411⟩

Share

Metrics

Record views

594

Files downloads

453