Open AccessProceedings Article
implicit Online Learning with Kernels
Li Cheng,Dale Schuurmans,Shaojun Wang,Terry Caelli,S. V. N. Vishwanathan +4 more
- 04 Dec 2006
- Vol. 19, pp 249-256
TL;DR: A new, implicit update technique that can be applied to a wide variety of convex loss functions and a bounded memory version, SILK, that maintains a compact representation of the predictor without compromising solution quality, even in non-stationary environments are introduced.
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Abstract: We present two new algorithms for online learning in reproducing kernel Hilbert spaces. Our first algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that can be applied to a wide variety of convex loss functions. We then introduce a bounded memory version, SILK (sparse ILK), that maintains a compact representation of the predictor without compromising solution quality, even in non-stationary environments. We prove loss bounds and analyze the convergence rate of both. Experimental evidence shows that our proposed algorithms outperform current methods on synthetic and real data.
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