Learning using hidden information (learning with teacher)

Authors:
Vladimir Vapnik;Akshay Vashist;Natalya Pavlovitch
Affiliations:
NEC Labs America and Columbia University;NEC Labs America, Princeton, NJ;Institute of Languages, Volhonka, Moscow, Russia
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

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 ∈ X, xi ∈ X, yi ∈ {-1, 1}, i = 1, ..., l. 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.