A false acceptance error controlling method for hyperspherical classifiers

Chen Wen Yen, Chieh Neng Young, Mark L. Nagurka

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error.

Original languageEnglish
Pages (from-to)295-312
Number of pages18
JournalNeurocomputing
Volume57
Issue number1-4
DOIs
StatePublished - 1 Mar 2004
Externally publishedYes

Keywords

  • Hyperspherical classifiers
  • Pattern recognition
  • Self-organization
  • Unknown pattern rejection

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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