Neural and statistical classifiers-taxonomy and two case studies

Authors:
L. Holmstrom;P. Koistinen;J. Laaksonen;E. Oja
Affiliations:
Rolf Nevanlinna Inst., Helsinki Univ.;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
1997

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Abstract

Pattern classification using neural networks and statistical methods is discussed. We give a tutorial overview in which popular classifiers are grouped into distinct categories according to their underlying mathematical principles; also, we assess what makes a classifier neural. The overview is complemented by two case studies using handwritten digit and phoneme data that test the performance of a number of most typical neural-network and statistical classifiers. Four methods of our own are included: reduced kernel discriminant analysis, the learning k-nearest neighbors classifier, the averaged learning subspace method, and a version of kernel discriminant analysis