Functional Regularized Least Squares Classication with Operator-Valued Kernels
Abstract
Although operator-valued kernels have recently received increasing interest in various machine learning and functional data analysis problems such as multi-task learning or functional regression, little attention has been paid to the understanding of their associated feature spaces. In this paper, we explore the potential of adopting an operator-valued kernel feature space perspective for the analysis of functional data. We then extend the Regularized Least Squares Classification (RLSC) algorithm to cover situations where there are multiple functions per observation. Experiments on a sound recognition problem show that the proposed method outperforms the classical RLSC algorithm.
Cite
Text
Kadri et al. "Functional Regularized Least Squares Classication with Operator-Valued Kernels." International Conference on Machine Learning, 2011.Markdown
[Kadri et al. "Functional Regularized Least Squares Classication with Operator-Valued Kernels." International Conference on Machine Learning, 2011.](https://mlanthology.org/icml/2011/kadri2011icml-functional/)BibTeX
@inproceedings{kadri2011icml-functional,
title = {{Functional Regularized Least Squares Classication with Operator-Valued Kernels}},
author = {Kadri, Hachem and Rabaoui, Asma and Preux, Philippe and Duflos, Emmanuel and Rakotomamonjy, Alain},
booktitle = {International Conference on Machine Learning},
year = {2011},
pages = {993-1000},
url = {https://mlanthology.org/icml/2011/kadri2011icml-functional/}
}