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              <p>We present a new method for link prediction based on output kernel regression. This recent framework allows to convert the problem of learning a pairwise classifier into the task of learning a single output kernel regressor. A new family of models, with both input and output kernels, are learnt by minimizing least square loss penalized by a L2 norm. Both in the supervised and the semi-supervised case, we obtain closed-form solutions as for "classic" kernel ridge regression. Experimental results on protein-protein interaction networks show that the new methods exhibit better performances.</p>
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