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Domain adaptation in regression

ALT, (2011): 308-323

Cited by: 54|Views127
EI

Abstract

This paper presents a series of new results for domain adaptation in the regression setting. We prove that the discrepancy is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by a universal kernel such as the Gaussian kernel. We give new pointwise loss guarantees based on the discrepa...More

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