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Random Features for Large-Scale Kernel Machines

is that algorithms access the data only through evaluations of k(x,y), or through the kernel ma-trix consisting of k applied to all pairs of datapoints. As a result, large training sets incur large computational and storage costs. Instead of relying on the implicit lifting provided by the kernel trick, we propose explicitly mapping

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  Feature, Trix, Ma trix

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