Learning from a Population of Hypotheses

Authors:
Michael Kearns;H. Sebastian Seung
Affiliations:
AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, New Jersey 07974. mkearns@research.att.com;AT&T Bell Laboratories, 600 Mountain Avenue, Murray Hill, New Jersey 07974. seung@physics.att.com
Venue:
Machine Learning - Special issue on COLT '93
Year:
1995

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Abstract

We introduce a new formal model in which a learning algorithm must combine a collection of potentially poor but statistically independent hypothesis functions in order to approximate an unknown target function arbitrarily well. Our motivation includes the question of how to make optimal use of multiple independent runs of a mediocre learning algorithm, as well as settings in which the many hypotheses are obtained by a distributed population of identical learning agents.