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<https://discovery.ucl.ac.uk/id/eprint/399130> <http://purl.org/dc/terms/title> "Sparse Semi-supervised Learning Using Conjugate Functions"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/399130> <http://purl.org/ontology/bibo/abstract> "In this paper, we propose a general framework for sparse semi-supervised learning, which concerns\r\nusing a small portion of unlabeled data and a few labeled data to represent target functions and thus\r\nhas the merit of accelerating function evaluations when predicting the output of a new example.\r\nThis framework makes use of Fenchel-Legendre conjugates to rewrite a convex insensitive loss\r\ninvolving a regularization with unlabeled data, and is applicable to a family of semi-supervised\r\nlearning methods such as multi-view co-regularized least squares and single-view Laplacian support\r\nvector machines (SVMs). As an instantiation of this framework, we propose sparse multi-view\r\nSVMs which use a squared ε-insensitive loss. The resultant optimization is an inf-sup problem and\r\nthe optimal solutions have arguably saddle-point properties. We present a globally optimal iterative\r\nalgorithm to optimize the problem. We give the margin bound on the generalization error of the\r\nsparse multi-view SVMs, and derive the empirical Rademacher complexity for the induced function\r\nclass. Experiments on artificial and real-world data show their effectiveness. We further give a\r\nsequential training approach to show their possibility and potential for uses in large-scale problems\r\nand provide encouraging experimental results indicating the efficacy of the margin bound and empirical\r\nRademacher complexity on characterizing the roles of unlabeled data for semi-supervised\r\nlearning"^^<http://www.w3.org/2001/XMLSchema#string> .
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