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A Multi-layerd Immune Inspired Machine Learning Algorithm

Knight, Thomas and Timmis, Jon (2003) A Multi-layerd Immune Inspired Machine Learning Algorithm. In: Lotfi, Ahmed and Garibaldi, M., eds. Applications and Science in Soft Computing. Springer, pp. 195-202. ISBN 978-3-540-40856-7. (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:13866)

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://www.cs.kent.ac.uk/pubs/2003/1760

Abstract

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:00 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13866 (The current URI for this page, for reference purposes)
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