Pattern classification as an ill-posed, inverse problem: a regularization approach

Authors:
Paul Yee;Simon Haykin
Affiliations:
Communications Research Laboratory, McMaster University, Hamilton, Ontario;Communications Research Laboratory, McMaster University, Hamilton, Ontario
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: plenary, special, audio, underwater acoustics, VLSI, neural networks - Volume I
Year:
1993

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Abstract

Pattern classification may be viewed as an ill-posed, inverse problem to which the method of regularization be applied. In doing so, a proper theoretical framework is provided for the application of radial basis function (RBF) networks to pattern classification, with strong links to the classical kernel regression estimator (KRE)-based classifiers that estimate the underlying posterior class densities.