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Traditional online kernel learning analysis assumes independently identically distributed (i.i.d.) about the training sequence. Recent studies reveal that when the loss function is smooth and strongly convex, given $T$ i.i.d. training instances, a constant sampling complexity of random Fourier features is sufficient to ensure $O(\log T/T)$ convergence rate of excess risk, which is optimal in online...
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