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Convergence rates of Kernel Conjugate Gradient for random design regression
We prove statistical rates of convergence for kernel-based least squares regression from i. i. d.
Optimal learning rates for Kernel Conjugate Gradient regression
Lower bounds on attainable rates depending on these two quantities were established in earlier literature, and we obtain upper bounds for the considered method that match these lower bounds (up to a log factor) if the true regression function belongs to the reproducing kernel Hilbert space.
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