Nonlinear Functional Regression: A Functional RKHS Approach
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.
Cite
Text
Kadri et al. "Nonlinear Functional Regression: A Functional RKHS Approach." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Kadri et al. "Nonlinear Functional Regression: A Functional RKHS Approach." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/kadri2010aistats-nonlinear/)BibTeX
@inproceedings{kadri2010aistats-nonlinear,
title = {{Nonlinear Functional Regression: A Functional RKHS Approach}},
author = {Kadri, Hachem and Duflos, Emmanuel and Preux, Philippe and Canu, Stéphane and Davy, Manuel},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {374-380},
volume = {9},
url = {https://mlanthology.org/aistats/2010/kadri2010aistats-nonlinear/}
}