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International Conference on Neural Information Processing

ICONIP 2009: Neural Information Processing pp 710–719Cite as

An Algorithm Based on the Construction of Braun’s Cathode Ray Tube as a Novel Technique for Data Classification

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

Abstract

In this article we have presented a model used for a classification of multidimensional data in a broader sense, called Braun’s cathode machine. The internal structure of the machine presented on this paper has been based on the architecture of a cathode-ray tube – Braun’s tube. For a machine model described this way a machine training algorithm has been proposed as well as response computing algorithms. In the final chapter we have presented the results of the machine tests for the notions connected with the classification and self-organization of multidimensional data.

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Modelling, Cracow University of Technology, Cracow, Poland

    Mariusz Swiecicki

Authors
  1. Mariusz Swiecicki
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

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© 2009 Springer-Verlag Berlin Heidelberg

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Swiecicki, M. (2009). An Algorithm Based on the Construction of Braun’s Cathode Ray Tube as a Novel Technique for Data Classification. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_79

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  • DOI: https://doi.org/10.1007/978-3-642-10684-2_79

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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