Online Learning with Kernels

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.

Authors

• Jyrki Kivinen
• Alex Smola
• Robert Williamson

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