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University of Illinois at Urbana-Champaign
New Algorithms for Attribute-Efficient on -Line Linear Learning
Abstract
dc:descriptionAnother 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Harris, Harlan D.
- Contributors dc:contributor
-
- Gary Dell
- Roth, Dan
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3101855
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81622