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# A survey on online kernel selection for online kernel learning

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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
Kernel learning for classification, where the data cannot be separated using a linear function in the input space, but can be in the kernel‐induced feature space
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Online kernel selection in a nested sequence of RKHSs $ℋκ4⊆ℋκ3⊆ℋκ2⊆ℋκ1$, where f t is the hypothesis obtained by online kernel selection at round t and f* is the competing hypothesis that we want to compare against
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Comparison between offline model selection (left) and online model selection at a single round (right)
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Comparison on the average mistake rates of online classification by kernelized online gradient descent (KOGD) and kernel perceptron (KP), when using different Gaussian kernels on three benchmark datasets. (a) german, (b) spambase, and (c) mushrooms
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