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INVESTIGATING FEEDFORWARD NEURAL NETWORKS WITH RESPECT TO THE REJECTION OF SPURIOUS PATTERNS

Vasconcelos, G.C., Fairhurst, Michael, Bisset, D.L. (1995) INVESTIGATING FEEDFORWARD NEURAL NETWORKS WITH RESPECT TO THE REJECTION OF SPURIOUS PATTERNS. Pattern Recognition Letters, 16 (2). pp. 207-212. ISSN 0167-8655. (doi:10.1016/0167-8655(94)00092-h) (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:19104)

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:
https://doi.org/10.1016/0167-8655(94)00092-h

Abstract

The reliability of feedforward neural networks with respect to the rejection of patterns not belonging to the defined training classes is investigated. It is shown how networks with different activation functions and propagation rules construct the decision regions in the pattern space and, therefore, affect the network's performance in dealing with spurious information. A modification to the standard MLP structure is described to enhance its reliability in this respect.

Item Type: Article
DOI/Identification number: 10.1016/0167-8655(94)00092-h
Uncontrolled keywords: FEEDFORWARD NEURAL NETWORKS; PATTERN CLASSIFICATION
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: I.T. Ekpo
Date Deposited: 22 May 2009 14:58 UTC
Last Modified: 09 Mar 2023 11:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/19104 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

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