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A new model for classifying DNA code inspired by neural networks and FSA

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Kang, BH and Kelarev, A and Sale, AHJ and Williams, R (2006) A new model for classifying DNA code inspired by neural networks and FSA. In: Advances in Knowledge Acquisition and Management. Lecture Notes in Computer Science (4303). Springer, Berlin, pp. 187-198.

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Abstract

This paper introduces a new model of classifiers CL(V,E,l,r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. Our careful and thorough verification demonstrates that the classifiers CL(V,E,l,r) are general enough and will be capable of solving all classification tasks for any given DNA dataset. We develop a minimisation algorithm for these classifiers and include several open questions which could benefit from contributions of various researchers throughout the world.

Item Type: Book Section
Journal or Publication Title: Lecture Notes in Computer Science
Publisher: Springer
Page Range: pp. 187-198
Identification Number - DOI: 10.1007/11961239_17
Additional Information: The original publication is available at www.springerlink.com
Date Deposited: 04 Jan 2007
Last Modified: 18 Nov 2014 03:12
URI: http://eprints.utas.edu.au/id/eprint/445
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