Learning flexible concepts from examples: Employing the ideas of two-tiered concept representation

This thesis describes an exploration of methods involved in learning flexible concepts that is an important and less explored area in machine learning. The two approaches described in this thesis are based on the idea of two-tiered (TT) concept representations. In the TT representation, the first tier, called the Base Concept Representation (BCR), contains an explicit description of core concept properties, and the second tier, called the Inferential Concept Interpretation (ICI), defines allowable modifications of the explicit meaning and its dependence on the context of discourse.

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

2011

Author dc:creator

• Zhang, Jianping

Contributors dc:contributor

• Michalski, R.S.

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1990 Zhang, Jianping

Language dc:language

eng

Identifier

AAI9114481
(UMI)AAI9114481

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19027