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Online learning with kernels

Kivinen, Jyrki; Smola, Alexander; Williamson, Robert


Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time applications. In this paper, we consider online learning in a reproducing kernel Hilbert space. By considering classical stochastic gradient...[Show more]

CollectionsANU Research Publications
Date published: 2004
Type: Journal article
Source: IEEE Transactions on Signal Processing
DOI: 10.1109/TSP.2004.830991


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