Herbster, M;
(2001)
Learning additive models online with fast evaluating kernels.
In:
(pp. pp. 444-460).
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Abstract
We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time pertrial is possible for such algorithms as the Kernel-Adatron, the Kernel-Perceptron, and ROMMA for specific additive models. Second, we show that the techniques of the recent algorithms developed for online linear prediction when the best predictor changes over time may be implemented for kernel-based learners at no additional asymptotic cost. Finally, we introduce a new online kernel-based learning algorithm for which we give worst-case loss bounds for the ϵ-insensitive square loss.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning additive models online with fast evaluating kernels |
| ISBN-13: | 9783540423430 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/3-540-44581-1_29 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/135665 |
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