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- W2040468026 abstract "We consider a fully online regression algorithm associated with a general convex loss function and Gaussian kernels with changing variances. Error analysis is conducted in a setting with samples drawn from a non-identical sequence of probability measures. When a fixed Gaussian is used, it was known that the learning ability of induced algorithms is weak. By allowing varying Gaussians, we show that the achieved learning rates can be of polynomial decays." @default.
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- W2040468026 date "2011-10-01" @default.
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- W2040468026 title "ONLINE REGRESSION WITH VARYING GAUSSIANS AND NON-IDENTICAL DISTRIBUTIONS" @default.
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- W2040468026 doi "https://doi.org/10.1142/s0219530511001923" @default.
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