Learning using hidden information (Learning with teacher)

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 (x_i, y_i), x_i isin X, y_i 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 X a decision rule better than the one found in the classical paradigm.