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Title:New Algorithms for Attribute-Efficient on -Line Linear Learning
Author(s):Harris, Harlan D.
Doctoral Committee Chair(s):Gary Dell; Roth, Dan
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:Another significant goal of this work was to identify the inductive biases of each algorithm, so that they can be fairlycompared with each other. By examining their biases and properties using the results presented here, it is possible to view 2Pes as a particular generalization of the Winnow algorithm, and IDBD as a further generalization of 2Pes. Understanding these relationships furthers the potential of attribute-efficient algorithms for real-world applications.
Issue Date:2003
Type:Text
Language:English
Description:90 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.
URI:http://hdl.handle.net/2142/81622
Other Identifier(s):(MiAaPQ)AAI3101855
Date Available in IDEALS:2015-09-25
Date Deposited:2003


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