Online Learning with Kernels
Abstract
We consider online learning in a Reproducing Kernel Hilbert Space. Our method is computationally efficient and leads to simple algorithms. In particular we derive update equations for classification, regression, and novelty detection. The inclusion of the -trick allows us to give a robust parameterization. Moreover, unlike in batch learning where the -trick only applies to the -insensitive loss function we are able to derive gen- eral trimmed-mean types of estimators such as for Huber’s robust loss.
Cite
Text
Kivinen et al. "Online Learning with Kernels." Neural Information Processing Systems, 2001.Markdown
[Kivinen et al. "Online Learning with Kernels." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/kivinen2001neurips-online/)BibTeX
@inproceedings{kivinen2001neurips-online,
title = {{Online Learning with Kernels}},
author = {Kivinen, Jyrki and Smola, Alex J. and Williamson, Robert C.},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {785-792},
url = {https://mlanthology.org/neurips/2001/kivinen2001neurips-online/}
}