A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications

Lorrentz, Pierre and Howells, Gareth and McDonald-Maier, Klaus D. (2010) A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications. Neural Process Letters, 31 . pp. 25-44. (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://dx.doi.org/10.1007/s11063-009-9125-1

Abstract

Artificial neural systems in general and weightless systems in particular have traditionally struggled in performance terms when confronted with problem domains such as possessing a large number of independent pattern classes and pattern classes with non-standard distributions. A multi-classifier is proposed which explores problem domains with a large number of independent pattern classes typically found in forensic and security databases. Specifically, the multi-classifier system is demonstrated on the exemplar of fingerprint identification problem typical to forensic, biometric, and security. Furthermore, the multi-classifier is able to provide a reasonable solution to benchmark problems from medicinal and physical (science) fields, which are determining the health, state of thyroid glands and determining whether or not there is a structure in the ionosphere, respectively.

Item Type: Article
Uncontrolled keywords: Combiner unit, enhanced probabilistic convergent network, fingerprints, computational intelligent fusion, ionosphere, multi-classifier, thyroid glands
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 08 Apr 2010 09:43
Last Modified: 14 Jul 2014 08:52
Resource URI: https://kar.kent.ac.uk/id/eprint/24224 (The current URI for this page, for reference purposes)
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