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

Pattern classification can be viewed as an ill-posed, inverse problem to which the method of regularization can 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. Assuming that the training patterns …