Strategies for combining classifiers employing shared and distinct pattern representations

Kittler, Josef, Hojjatoleslami, Ali, Windeatt, T. (1997) Strategies for combining classifiers employing shared and distinct pattern representations. Pattern Recognition Letters, 18 (11-13). pp. 1373-1377. ISSN 0167-8655. (doi:10.1016/S0167-8655(97)00095-0) (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:27608)

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.
Official URL: http://dx.doi.org/10.1016/S0167-8655(97)00095-0

Abstract

The problem of combining multiple classifiers which employ mixed mode representations consisting of some shared and some distinct features is studied. Two combination strategies are developed and experimentally compared on mammographic data to demonstrate their effectiveness.

Item Type:	Article
DOI/Identification number:	10.1016/S0167-8655(97)00095-0
Uncontrolled keywords:	Classification; Multiple expert function
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts Divisions > Division of Natural Sciences > Biosciences Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders:	Engineering and Physical Sciences Research Council (https://ror.org/0439y7842)
Depositing User:	S.A. Hojjatoleslami
Date Deposited:	19 May 2011 11:54 UTC
Last Modified:	12 Jul 2022 10:40 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/27608 (The current URI for this page, for reference purposes)

University of Kent Author Information

Hojjatoleslami, Ali.

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