Lorrentz, P. and Howells, W.G.J. and McDonald-Maier, K.D. (2010) A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications. Neural Process Letters, 31 . pp. 25-44.
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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: | Jenny Harries |
| Date Deposited: | 08 Apr 2010 09:43 |
| Last Modified: | 08 Apr 2010 09:43 |
| Resource URI: | http://kar.kent.ac.uk/id/eprint/24224 (The current URI for this page, for reference purposes) |
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