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Automatic self-configuration of a novel multiple-expert classifier using a genetic algorithm

Rahman, Ahmad Fuad Rezaur and Fairhurst, Michael (1999) Automatic self-configuration of a novel multiple-expert classifier using a genetic algorithm. In: Seventh International Conference on Image Processing And Its Applications, 1999. IEEE, pp. 57-61. ISBN 0-85296-717-9. (doi:10.1049/cp:19990281) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1049/cp:19990281

Abstract

A novel self-configurable multiple expert classifier is introduced. A genetic algorithm has been employed to automate the process of optimisation in terms of structural configuration. Application of this concept of self-configuration has produced very promising results in recognising a database of machine printed characters by the proposed multiple expert configuration.

Item Type: Book section
DOI/Identification number: 10.1049/cp:19990281
Additional information: Issue: 465; Proceedings Paper
Uncontrolled keywords: automatic self-configuration; multiple-expert classifier; genetic algorithm; optimisation; structural configuration; machine printed characters database; multiple expert configuration
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunications > TK5103.59 Optical communications, Fibre-optics
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: F.D. Zabet
Date Deposited: 17 Apr 2009 18:52 UTC
Last Modified: 09 Aug 2019 10:38 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/16510 (The current URI for this page, for reference purposes)
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