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In this paper we consider a new paradigm of learning: learning using hidden information. The classical paradigm of the supervised learning is to learn a decision rule from labeled data (xi, yi), xi isin X, yi isin {-1, 1}, i = 1, hellip, lscr. In this paper we consider a new setting: given training vectors in space X along with labels and description of this data in another space X*, find in space...
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