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Bounds for Vector-Valued Function Estimation (1606.01487v1)
Published 5 Jun 2016 in stat.ML and cs.LG
Abstract: We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under which shared representations are beneficial for multi- task learning are equally applicable to multi-category learning.