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On AIRS and Clonal Selection for Machine Learning

  • Chris McEwan
  • Emma Hart
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5666)

Abstract

AIRS is an immune-inspired supervised learning algorithm that has been shown to perform competitively on some common datasets. Previous analysis of the algorithm consists almost exclusively of empirical benchmarks and the reason for its success remains somewhat speculative. In this paper, we decouple the statistical and immunological aspects of AIRS and consider their merits individually. This perspective allows us to clarifying why AIRS performs as it does and identify deficiencies that leave AIRS lacking. A comparison with Radial Basis Functions suggests that each may have something to offer the other.

Keywords

Radial Basis Function Memory Cell Clonal Selection Immune Network Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chris McEwan
    • 1
  • Emma Hart
    • 1
  1. 1.Napier UniversityEdinburgh

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