Learning flexible concepts from examples: Employing the ideas of two-tiered concept representation
Zhang, Jianping
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/19027
Description
Title
Learning flexible concepts from examples: Employing the ideas of two-tiered concept representation
Author(s)
Zhang, Jianping
Issue Date
1990
Doctoral Committee Chair(s)
Michalski, R.S.
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Computer Science
Language
eng
Abstract
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.
The first approach generates a BCR of a concept by first creating a complete and consistent (CC) description from supplied training examples, and then optimizing the description according to a general description quality (GDQ) criterion. The CC description is obtained by applying the AQ inductive learning methodology. The optimization process is done by a double-level best-first search, using the SG-TRUNC description reduction process. The ICI consists of a user-specified procedure of flexible matching and a set of inference rules. This approach has been implemented in the POSEIDON learning system, and experimentally tested on two different real-world problems.
In the second approach, a concept is presented as a set of extended complexes and a set of exemplars. Each extended complex is a generalized description and consists of a base complex (equivalent to a disjunct), a similarity measure, a threshold, and a set of weights. Accordingly, the task of learning a concept is to create a set of extended complexes and a set of exemplars. An extended complex is generated in two phases: base complex generation and extended complex optimization. When generating an extended complex, two evaluation functions, quality evaluation function and potential quality evaluation function are applied. This approach has been implemented in FCLS that is tested in various domains including artificial and real world domains.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
