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Forward Models: Supervised Learning with a Distal Teacher
Author(s) -
Jordan Michael I.,
Rumelhart David E.
Publication year - 1992
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog1603_1
Subject(s) - computer science , supervised learning , artificial intelligence , machine learning , semi supervised learning , internal model , adaptive learning , unsupervised learning , artificial neural network , control (management)
Internal models of the environment have an important role to play in adaptive systems, in general, and are of particular importance for the supervised learning paradigm. In this article we demonstrate that certain classical problems associated with the notion of the “teacher” in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multilayer networks.