A survey on online kernel selection for online kernel learning

Online kernel selection is fundamental to online kernel learning. In contrast to offline kernel selection, online kernel selection intermixes kernel selection and training at each round of online kernel learning, and requires a sublinear regret bound and low computational complexity. In this paper, we first compare the difference between offline kernel selection and online kernel selection, then survey existing online kernel selection approaches from the perspectives of formulation, algorithm, candidate kernels, computational complexities and regret guarantees, and finally point out some future research directions in online kernel selection. This article is categorized under: Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining