Online Learning with Kernels

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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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.