Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Jyrki Kivinen, Alex Smola, Robert C. Williamson
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.