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Immune Inspired Information Filtering in a High Dimensional Space

  • Nikolaos Nanas
  • Stefanos Kodovas
  • Manolis Vavalis
  • Elias Houstis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6209)

Abstract

Adaptive Information Filtering is a challenging computational problem that requires a high dimensional feature space. However, theoretical issues arise when vector-based representations are adopted in such a space. In this paper, we use AIF as a test bed to provide experimental evidence indicating that the learning abilities of vector-based Artificial Immune Systems are diminished in a high dimensional space.

Keywords

Relevant Document High Dimensional Space Relevance Score High Dimensional Feature Space Evaluation Cycle 
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 2010

Authors and Affiliations

  • Nikolaos Nanas
    • 1
    • 2
  • Stefanos Kodovas
    • 1
    • 2
  • Manolis Vavalis
    • 1
    • 2
  • Elias Houstis
    • 1
    • 2
  1. 1.Lab for Information Systems and ServicesCentre for Research and Technology - Thessaly (CE.RE.TE.TH) 
  2. 2.Computing and Telecomunications DepartmentUniversity of Thessaly 

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