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Lecture Notes in Computer Science
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method called SMTR, which is based on Gaussian processes (GP) with the assumption that the kernel parametersdoi:10.1007/978-3-642-04174-7_40 fatcat:hficq3a67zfz5px2rfvkavhvki