Extensions of Gaussian Processes for Ranking : Semi-supervised and Active Learning

Unlabelled examples in supervised learning tasks can be optimally exploited using semi-supervised methods and active learning. We focus on ranking learning from pairwise instance preference to discuss these important extensions, semi-supervised learning and active learning, in the probabilistic framework of Gaussian processes. Numerical experiments demonstrate the capacities of these techniques.

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