An Improved Training Algorithm for Kernel Fisher Discriminants

Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf

AISTATS 2001 pp. 209-215

/aistats/2001/mika2001aistats-improved/

Abstract

We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the art by more than an order of magnitude, thus rendering the kernel Fisher algorithm a viable option also for large datasets.

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Cite

Text

Mika et al. "An Improved Training Algorithm for Kernel Fisher Discriminants." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.

Markdown

[Mika et al. "An Improved Training Algorithm for Kernel Fisher Discriminants." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/mika2001aistats-improved/)

BibTeX

@inproceedings{mika2001aistats-improved,
  title     = {{An Improved Training Algorithm for Kernel Fisher Discriminants}},
  author    = {Mika, Sebastian and Smola, Alexander J. and Schölkopf, Bernhard},
  booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
  year      = {2001},
  pages     = {209-215},
  volume    = {R3},
  url       = {https://mlanthology.org/aistats/2001/mika2001aistats-improved/}
}