Inductive character learning and classification with generic algorithms

Alastair D. McAulay, Jae Chan Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Adaptive-image learning and discrimination techniques using classifier systems are presented. The genetic algorithm (GA) is used for a learning strategy in the system. The proposed system learns arbitrary image objects without any prior knowledge of given images and recognizes them. The system also makes up for some general weak points that are present in most learning systems including conventional classifier systems. That is, first, in a learning system, forgetting of knowledge usually occurs if the knowledge is not used for a long time period. The system still maximizes adaptability, but it prevents the system from forgetting useful rules by using the No_Unlearn mode. Second, to improve large-class image classification and learning, a multiple sublength concept has been introduced to genetic algorithms. Third, a triggered GA, which plays an important role in distinguishing two or more similar images by eliminating generalists, is developed.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Systems Engineering
PublisherIEEE Computer Society
Pages363-366
Number of pages4
ISBN (Print)0780301730
StatePublished - Dec 1 1991
Externally publishedYes
Event1991 IEEE International Conference on Systems Engineering - Fairborn, OH, USA
Duration: Aug 1 1991Aug 3 1991

Publication series

NameIEEE International Conference on Systems Engineering

Other

Other1991 IEEE International Conference on Systems Engineering
CityFairborn, OH, USA
Period8/1/918/3/91

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    McAulay, A. D., & Oh, J. C. (1991). Inductive character learning and classification with generic algorithms. In IEEE International Conference on Systems Engineering (pp. 363-366). (IEEE International Conference on Systems Engineering). IEEE Computer Society.