Step size-adapted online support vector learning
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005|
|01_Karatzoglou_Step_size-adapted_online_2005.pdf||282.09 kB||Adobe PDF||Request a copy|
Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.