A Comparative Study of KBS, ANN and Statistical Clustering Techniques for Unattended Stellar Classification

  • Carlos Dafonte
  • Alejandra Rodríguez
  • Bernardino Arcay
  • Iciar Carricajo
  • Minia Manteiga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

The purpose of this work is to present a comparative analysis of knowledge-based systems, artificial neural networks and statistical clustering algorithms applied to the classification of low resolution stellar spectra. These techniques were used to classify a sample of approximately 258 optical spectra from public catalogues using the standard MK system. At present, we already dispose of a hybrid system that carries out this task, applying the most appropriate classification method to each spectrum with a success rate that is similar to that of human experts.

Keywords

Expert System Spectral Parameter Input Pattern Spectral Type Human Expert 
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 2005

Authors and Affiliations

  • Carlos Dafonte
    • 1
  • Alejandra Rodríguez
    • 1
  • Bernardino Arcay
    • 1
  • Iciar Carricajo
    • 2
  • Minia Manteiga
    • 2
  1. 1.Information and Communications Technologies Department, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Navigation and Earth Sciences DeptartmentUniversity of A CoruñaA CoruñaSpain

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