title: Simple randomized algorithms for online learning with kernels.
abstract: In online learning with kernels, it is vital to control the size (budget) of the support set because of the curse of kernelization. In this paper, we propose two simple and effective stochastic strategies for controlling the budget. Both algorithms have an expected regret that is sublinear in the horizon. Experimental results on a number of benchmark data sets demonstrate encouraging performance in terms of both efficacy and efficiency.
authors.affiliation: Department of Mathematics and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Electronic address: hwwhbb@163.com.
authors.forename: Wenwu
authors.initials: W
authors.lastname: He
authors.affiliation: Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. Electronic address: jamesk@cse.ust.hk.
authors.forename: James T
authors.initials: JT
authors.lastname: Kwok
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D012984:Software
keywords: Budget; Kernel methods; Online learning; Stochastic strategies