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Method Of Key Vectors Extraction Using R-Cloud Classifiers

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Author Info

  • 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)

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    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 of R-clouds. The advantages of the R-cloud classification method introduced recently are being investigated. The separating boundary of the R-cloud classifier is represented using Rvachev functions. The method of key vectors extraction uses the value of the R-cloud function to quantify the disturbance of the separating boundary, which is caused by removal of one data vector from the design dataset. The R-cloud method was found instructive and practical in a number of engineering problems related to pattern classification.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 03 (2007)
    Issue (Month): 03 ()
    Pages: 419-426

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    Handle: RePEc:wsi:nmncxx:v:03:y:2007:i:03:p:419-426

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    Related research

    Keywords: Rvachev functions; R-clouds; classification; data set reduction; key vectors;

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