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Extension of HUMANN for Dealing with Noise and with Classes of Different Shape and Size: A Parametric Study

  • Patricio García Báez
  • Carmen Paz Suárez Araujo
  • Pablo Fernández López
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

In this paper an extension of HUMANN (hierarchical unsupervised modular adaptive neural network) is presented together with a parametric study of this network in dealing with noise and with classes of any shape and size. The study has been made based on the two most noise dependent HUMANN parameters, λ and μ, using synthesised databases (bidimensional patterns with outliers and classes with different probability density distribution). In order to evaluate the robustness of HUMANN a Monte Carlo [1] analysis was carried out using the creation of separate data in given classes. The influence of the different parameters in the recovery of these classes was then studied.

Keywords

Success Ratio Probability Density Distribution Outlier Data Tolerance Module Silent Synapse 
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 2001

Authors and Affiliations

  • Patricio García Báez
    • 1
  • Carmen Paz Suárez Araujo
    • 2
  • Pablo Fernández López
    • 2
  1. 1.Department of Statistics, Operating Research and ComputationUniversity of La LagunaCanary IslandsSpain
  2. 2.Department of Computer Sciences and SystemsUniversity of Las Palmas de Gran CanariaCanary IslandsSpain

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