Enhancing Multi-Neural Systems through the use of Hybrid Structures

Canuto, Anne, Fairhurst, Michael, Howells, Gareth (2003) Enhancing Multi-Neural Systems through the use of Hybrid Structures. In: Neural Networks, 2004. Proceedings. 2003 IEEE International Joint Conference on. . IEEE ISBN 0-7803-7898-9. (doi:10.1109/IJCNN.2003.1223364) (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:59312)

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://www.dx.doi.org/ 10.1109/IJCNN.2003.1223364

Abstract

This paper investigates the performance of multi-neural systems, focusing on the benefits that can be gained when integrating different types of neural experts (hybrid multi-neural system). An empirical evaluation shows that the integration of different types of neural networks leads to an improvement in performance in a practical classification task for a range of combination methods.

Item Type:	Conference or workshop item (Paper)
DOI/Identification number:	10.1109/IJCNN.2003.1223364
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Institutional Unit:	Schools > School of Engineering, Mathematics and Physics > Engineering
Former Institutional Unit:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	Gareth Howells
Date Deposited:	30 Nov 2016 18:35 UTC
Last Modified:	20 May 2025 10:41 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/59312 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

Creator's ORCID:
CReDIT Contributor Roles:

Howells, Gareth.

Creator's ORCID:	https://orcid.org/0000-0001-5590-0880
CReDIT Contributor Roles:

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