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Wavelet‐based estimation of a discriminant function
Author(s) -
Chang Woojin,
Kim SeongHee,
Vidakovic Brani
Publication year - 2003
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.498
Subject(s) - wavelet , consistency (knowledge bases) , discriminant , computer science , artificial intelligence , binary number , pattern recognition (psychology) , field (mathematics) , function (biology) , discriminant function analysis , linear discriminant analysis , mathematics , algorithm , mathematical optimization , machine learning , arithmetic , pure mathematics , evolutionary biology , biology
In this paper, we consider wavelet‐based binary linear classifiers. Both consistency results and implementational issues are addressed. We show that under mild assumptions on the design density wavelet discrimination rules are L 2 ‐consistent. The proposed method is illustrated on synthetic data sets in which the ‘truth’ is known and on an applied discrimination problem from the industrial field. Copyright © 2003 John Wiley & Sons, Ltd.