A finite set of data-adaptive basis functions is proven to represent the least-squares, Tikhonov, and conjugate-gradient estimators for learning convolution kernels, removing manual reproducing-kernel selection.
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Automatic reproducing kernel and regularization for learning convolution kernels
A finite set of data-adaptive basis functions is proven to represent the least-squares, Tikhonov, and conjugate-gradient estimators for learning convolution kernels, removing manual reproducing-kernel selection.