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Step size-adapted online support vector learning

Karatzoglou, Alexandros; Vishwanathan, S; Schraudolph, Nicol; Smola, Alexander


We present an online Support Vector Machine (SVM) that uses Stochastic Meta-Descent (SMD) to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient descent in Reproducing Kernel Hubert Space (RKHS) and translate SMD to the nonparametric setting, where its gradient trace parameter is no longer a coefficient vector but an element of the RKHS. We derive efficient updates that allow us to perform the step size adaptation in linear time. We apply the...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Conference paper
Source: Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005
DOI: 10.1109/ISSPA.2005.1581065


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