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A comparative study of decision combination strategies for a novel multiple-expert classifier

Rahman, Ahmad Fuad Rezaur and Fairhurst, Michael (1997) A comparative study of decision combination strategies for a novel multiple-expert classifier. In: 1997 Sixth International Conference on Image Processing and Its Applications. IEEE, pp. 131-135. ISBN 978-0-85296-692-1. (doi:10.1049/cp:19970869) (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) (KAR id:17896)

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:19970869

Abstract

The performance of a novel multiple expert decision combination strategy has been compared with other multiple expert decision combination methods reported in the literature. The concept of decision combination has been generalised in two different categories and it has been demonstrated how these different categories perform with respect to each other under optimised conditions. The paper presents the performance of this particular network, which is a Type-II network and compares it with other Type-I decision combination strategies previously reported in literature. These methods include aggregation method, choice selection and ranking method. In all the cases, the chosen database was the NIST database, which is recognised to be the standard database for handwritten characters. It has been found that this particular Type-II configuration is able to outperform all these Type-I combination strategies. The performance enhancement on a subset of the NIST database having a thousand character samples for each class has been found to be around 1.2% with respect to the best recognition performance obtained from either of the Type-I decision combination strategies investigated.

Item Type: Book section
DOI/Identification number: 10.1049/cp:19970869
Uncontrolled keywords: character recognition
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: T.J. Sango
Date Deposited: 21 May 2009 07:29 UTC
Last Modified: 18 Sep 2019 12:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/17896 (The current URI for this page, for reference purposes)
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