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University of Illinois at Urbana-Champaign

Matters Horn and other features in the computational learning theory landscape: The notion of membership

Abstract

dc:description

Current knowledge representation research has sought to provide schemes for encoding knowledge about how a given system behaves, with the goal being accuracy and utility. Ideally, the goal of encoding knowledge is not the task of encoding, but the product of the encoding task. If such encodings are required for a variety of systems, then question of automating the process of encoding arises.

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
Date dc:date
10000-01-01

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Frazier, Michael Duane
Contributors dc:contributor
  • Pitt, Leonard

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • Copyright 1994 Frazier, Michael Duane
Language dc:language
eng

Identifiers

dc:identifier.*
Identifier
AAI9503189
(UMI)AAI9503189
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/21745

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Frazier, Michael Duane. Matters Horn and other features in the computational learning theory landscape: The notion of membership. Dissertation thesis, University of Illinois at Urbana-Champaign, http://hdl.handle.net/2142/21745