A Sequential Decision Model for Selecting Feature Subsets in Pattern Recognition

Author

Chien, Yi-tzuu

Author_Institution

IEEE

Issue

3

fYear

1971

fDate

3/1/1971 12:00:00 AM

Firstpage

282

Lastpage

290

Abstract

This paper deals with a sequential decision model for selecting feature subsets in pattern recognition. Based upon this model, a number of selection strategies are proposed and their properties are investigated. The basic criterion of these selection strategies, which depend only on the last r (r finite) recognition samples, is to maximize the long-run proportion of correct recognition, the maximization being overall possible feature subsets. The sequential model is particularly suitable for the implementation of on-line selection processes often required in pattern recognition. A character recognition experiment has been simulated on a digital computer to demonstrate the feasibility of this approach.

Keywords

Computer-simulated experiment, feature subsets, pattern recognition, selection strategies, sequential decision model.; Adaptive systems; Character recognition; Computational modeling; Computer simulation; Extraterrestrial measurements; Medical diagnostic imaging; Pattern analysis; Pattern recognition; Probability distribution; Statistical distributions; Computer-simulated experiment, feature subsets, pattern recognition, selection strategies, sequential decision model.;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/T-C.1971.223232

Filename

1671825