Frames, Reproducing Kernels, Regularization and Learning
Abstract
This work deals with a method for building a reproducing kernel Hilbert space (RKHS) from a Hilbert space with frame elements having special properties. Conditions on existence and a method of construction are given. Then, these RKHS are used within the framework of regularization theory for function approximation. Implications on semiparametric estimation are discussed and a multiscale scheme of regularization is also proposed. Results on toy and real-world approximation problems illustrate the effectiveness of such methods.
Cite
Text
Rakotomamonjy and Canu. "Frames, Reproducing Kernels, Regularization and Learning." Journal of Machine Learning Research, 2005.Markdown
[Rakotomamonjy and Canu. "Frames, Reproducing Kernels, Regularization and Learning." Journal of Machine Learning Research, 2005.](https://mlanthology.org/jmlr/2005/rakotomamonjy2005jmlr-frames/)BibTeX
@article{rakotomamonjy2005jmlr-frames,
title = {{Frames, Reproducing Kernels, Regularization and Learning}},
author = {Rakotomamonjy, Alain and Canu, Stéphane},
journal = {Journal of Machine Learning Research},
year = {2005},
pages = {1485-1515},
volume = {6},
url = {https://mlanthology.org/jmlr/2005/rakotomamonjy2005jmlr-frames/}
}