METHOD OF KEY VECTORS EXTRACTION USINGR-CLOUD CLASSIFIERS

Anton Bougaev (), Aleksey Urmanov (), Lefteri Tsoukalas () and Kenny Gross ()
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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

New Mathematics and Natural Computation (NMNC), 2007, vol. 03, issue 03, 419-426

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.

Keywords: Rvachev functions; R-clouds; classification; data set reduction; key vectors (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1142/S1793005707000884

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