Learning from a Population of Hypotheses |
| |
Authors: | Kearns Michael Sebastian Seung H |
| |
Affiliation: | (1) AT&T Bell Laboratories, 600 Mountain Avenue, 07974 Murray Hill, New Jersey |
| |
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. |
| |
Keywords: | machine learning computational learning theory PAC learning learning agents |
本文献已被 SpringerLink 等数据库收录! |