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Found 4 papers, 4 papers with code
Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound
In this paper, we study the mistake bound of online kernel learning on a budget.
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression
The trade-off between regret and computational cost is a fundamental problem for online kernel regression, and previous algorithms worked on the trade-off can not keep optimal regret bounds at a sublinear computational complexity.
Improved Regret Bounds for Online Kernel Selection under Bandit Feedback
We apply the two algorithms to online kernel selection with time constraint and prove new regret bounds matching or improving the previous $O(\sqrt{T\ln{K}} +\Vert f\Vert^2_{\mathcal{H}_i}\max\{\sqrt{T},\frac{T}{\sqrt{\mathcal{R}}}\})$ expected bound where $\mathcal{R}$ is the time budget.
Improved Kernel Alignment Regret Bound for Online Kernel Learning
If the eigenvalues of the kernel matrix decay exponentially, then our algorithm enjoys a regret of $O(\sqrt{\mathcal{A}_T})$ at a computational complexity of $O(\ln^2{T})$.
