Electronic Resource
Springer
Machine learning
18 (1995), S. 255-276
ISSN:
0885-6125
Keywords:
machine learning
;
computational learning theory
;
PAC learning
;
learning agents
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
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.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00993412
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