Hierarchy and Convergance of Immune Networks: Basic Ideas and Premilinary Results

de Castro, Leandro N. and Timmis, Jon (2002) Hierarchy and Convergance of Immune Networks: Basic Ideas and Premilinary Results. In: 1st International Conference on Artificial Immune Systems, September 9th-11th 2002, University of Kent at Canterbury, UK. (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)

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aiNet is an artificial immune network model originally developed to perform automatic data compression. Combined with graph theoretical and statistical clustering techniques, aiNet is a powerful data clustering and classification tool. However, the original aiNet model suffers from the lack of a well-defined stopping criterion and an ad hoc approach to parameter initialization, prior to the training process. This paper has two main goals. First, by assessing convergence criteria employed in a class of artificial neural networks, a suitable stopping criterion can be created for aiNet. Secondly, the paper demonstrates that through the use of a cooling schedule for some of these user-defined parameters, it is not only possible to reduce the importance of their initial values, but also this leads to possible derivation of a hierarchical tree of immune networks. Due to the very limited space available, only the basic ideas of a novel convergence criterion, and an approach to develop a tree of aiNets will be presented, together with an illustrative example.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: artificial immune systems, clonal selection, immune network
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 17:59
Last Modified: 01 Jul 2014 10:26
Resource URI: https://kar.kent.ac.uk/id/eprint/13725 (The current URI for this page, for reference purposes)
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