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We present a probabilistic framework for transferring learning across tasks and between labeled and unlabeled data. The approach is based on Gaussian process (GP) prediction and incorporates both the geometry of the data and the similarity between tasks within a GP covariance, allowing Bayesian prediction in a natural way. We discuss the transfer of learning in a multitask scenario in the two cases where the underlying geometry is assumed to be the same across tasks and where different tasks are assumed to have independent geometric structures. We demonstrate the method on a number of real datasets, indicating that the semisupervised multitask approach can result in very significant improvements in performance when very few labeled training examples are available.
|Number of pages||12|
|Journal||IEEE Transactions on Neural Networks and Learning Systems|
|Publication status||Published - 1 Dec 2013|
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CLIMB: Advancing machine learning methodology for new classes of prediction problems.
1/04/10 → 31/03/11