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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)

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://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
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Gareth Howells
Date Deposited: 30 Nov 2016 18:35 UTC
Last Modified: 29 May 2019 18:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/59312 (The current URI for this page, for reference purposes)
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