Step size adaptation in reproducing kernel Hilbert space
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]
|Collections||ANU Research Publications|
|Source:||Journal of Machine Learning Research|
|Vishwanathan_Step2006.pdf||439.76 kB||Adobe PDF|
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