Opportunistic constructive induction: Using fragments of domain knowledge to guide construction

One subfield of machine learning is the induction of a representation of a concept from positive and negative examples of the concept. Given a set of training examples, the goal of the inductive system is to create a description capable of classifying the training examples, yet general enough to accurately predict the classification of unseen examples. Often the original attributes describing the instances are inadequate to capture important regularities in the concept. New descriptors, constructed through the application of operators to the original attributes, can provide the proper vocabulary to create concise concept representations at the right level of generalization to be highly predictive. Constructive induction is the process of generating and applying new descriptors during inductive learning.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Gunsch, Gregg Harold

Contributors dc:contributor

• Rendell, Larry A.

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1991 Gunsch, Gregg Harold

Language dc:language

eng

Identifier

AAI9136607
(UMI)AAI9136607

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/20262