Knight, T. and Timmis, Jon (2003) A Multi-layerd Immune Inspired Machine Learning Algorithm. In: Lotfi, A. and Garibaldi, M., eds. Applications and Science in Soft Computing. Springer, pp. 195-202. ISBN 978-3540408567.
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Artificial Immune Systems (AIS) have recently been proposed as an additional soft computing paradigm. This paper proposes a new multi-layered unsupervised machine learning algorithm inspired by the vertebrate immune system. The algorithm has been tested on benchmark data and has shown a great deal of potential for data reduction and clustering tasks. This paper presents an overview of the algorithm, drawing analogies to the vertebrae immune system where appropriate. Results are presented for three data sets and observations are made about the potential for adapting the algorithm for a continuous learning paradigm.
|Item Type:||Book section|
|Uncontrolled keywords:||artificial immune systems, machine learning|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||24 Nov 2008 18:00|
|Last Modified:||14 Mar 2009 22:05|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/13866 (The current URI for this page, for reference purposes)|
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