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Step size adaptation in reproducing kernel Hilbert space

Vishwanathan, S; Schraudolph, Nicol; Smola, Alexander


This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient descent in reproducing kernel Hilbert 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...[Show more]

CollectionsANU Research Publications
Date published: 2006-06
Type: Journal article
Source: Journal of Machine Learning Research


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