A minimum entropy approach to rule learning from examples

Author

Pitas, Ioannis ; Milios, Evangelos ; Venetsanopoulos, Anastasios N.

Author_Institution

Dept. of Electr. Eng., Thessaloniki Univ., Greece

Volume

22

Issue

4

fYear

1992

Firstpage

621

Lastpage

635

Abstract

Learning from examples uses specific instances (examples and counterexamples) to produce general rules. It is a convenient learning scheme in cases where the process of interviewing human experts and analyzing and formalizing their decision is very difficult or time consuming. The system proposed is capable of obtaining the rules that fit a set of examples and counterexamples based on the minimal entropy (ME) criterion. The system proposed can also set various parameters of the rule (e.g., thresholds) in such a way that entropy is minimized. The system can also handle incremental learning from examples. Applications of the proposed system to seismic image analysis are included

Keywords

computerised pattern recognition; entropy; information theory; knowledge based systems; learning systems; artificial intelligence; decision rules; incremental learning; inductive learning; knowledge based systems; machine learning; minimum entropy; rule estimation; rule learning from examples; seismic image analysis; Artificial intelligence; Computer science; Entropy; Helium; Humans; Image analysis; Logic; Neck; Polynomials; Protocols;

fLanguage

English

Journal_Title

Systems, Man and Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

0018-9472

Type

jour

DOI

10.1109/21.156576

Filename

156576