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Method Of Key Vectors Extraction Usingr-Cloud Classifiers

Author

Listed:
  • ANTON BOUGAEV

    (School of Nuclear Engineering, Purdue University, W. Lafayette, IN, 47907, USA)

  • ALEKSEY URMANOV

    (Sun Microsystems, Inc. San Diego, CA, 92121, USA)

  • LEFTERI TSOUKALAS

    (School of Nuclear Engineering, Purdue University, W. Lafayette, IN, 47907, USA)

  • KENNY GROSS

    (Sun Microsystems, Inc., San Diego, CA, 92121, USA)

Abstract

A novel method for reducing a training data set in the context of nonparametric classification is proposed. The new method is based on the method ofR-clouds. The advantages of theR-cloud classification method introduced recently are being investigated. The separating boundary of theR-cloud classifier is represented using Rvachev functions. The method of key vectors extraction uses the value of theR-cloud function to quantify the disturbance of the separating boundary, which is caused by removal of one data vector from the design dataset. TheR-cloud method was found instructive and practical in a number of engineering problems related to pattern classification.

Suggested Citation

  • Anton Bougaev & Aleksey Urmanov & Lefteri Tsoukalas & Kenny Gross, 2007. "Method Of Key Vectors Extraction Usingr-Cloud Classifiers," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 419-426.
    • Handle: RePEc:wsi:nmncxx:v:03:y:2007:i:03:n:s1793005707000884
    DOI: 10.1142/S1793005707000884
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