title: Neural and statistical classifiers-taxonomy and two case studies.
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.
authors.affiliation: Rolf Nevanlinna Inst., Helsinki Univ.
authors.forename: L
authors.initials: L
authors.lastname: Holmstrom
authors.forename: P
authors.initials: P
authors.lastname: Koistinen
authors.forename: J
authors.initials: J
authors.lastname: Laaksonen
authors.forename: E
authors.initials: E
authors.lastname: Oja