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Title:Learning From a Monotonous, Ignorant Teacher
Author(s):Mishra, Nina
Doctoral Committee Chair(s):Pitt, Leonard
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Artificial Intelligence
Abstract:The third learning setting combines the first and second in that we both give our computer preclassified examples and allow it to pose membership queries. The unknown function f may now classify examples in one of three possible ways: "+", "$-$" or "?". We prove general results for when it is possible for a computer to learn to be ignorant in this three-valued setting (+, $-$, ?) that utilizes results from the two-valued (+, $-$) setting. The three-valued setting we consider differs from the standard three-valued setting in that the set of possible functions that f could be, together with the set of "+", "$-$" examples determine which examples can be classified "?".
Issue Date:1997
Type:Text
Language:English
Description:108 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.
URI:http://hdl.handle.net/2142/81903
Other Identifier(s):(MiAaPQ)AAI9812710
Date Available in IDEALS:2015-09-25
Date Deposited:1997


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