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"Generalized radial basis function networks for classification and novelty detection: Self-organization of optimal Bayesian decision" S. Albrecht, J. Busch, M. Kloppenburg, F. Metze und P. TavanNeural Networks 13: 1075-1093 (2000) Abstract: By adding reverse connections from the output layer to the central layer it is shown how a generalized radial basis functions (GRBF) network can self-organize to form a Bayesian classifier which is also capable of novelty detection. For that purpose three stochastic sequential learning rules are introduced from biological considerations which pertain to the centers, the shapes, and the widths of the receptive fields of the neurons and allow a joint optimization of all network parameters. The rules are shown to generate maximum likelihood estimates of the class-conditional probability density functions of labeled data in terms of multivariate normal mixtures. Upon combination with a hierarchy of deterministic annealing procedures, which implement a multiple scale approach, the learning process can avoid the convergence problems hampering conventional expectation-maximization algorithms. Using an example from the field of speech recognition the stages of the learning process and the capabilities of the self-organizing GRBF classifier are illustrated. BMO authors (in alphabetic order): Sebastian Albrecht Jan Busch Martin Kloppenburg Florian Metze Paul Tavan Assoziierte Projekte: Selforganized pattern classification and its application to molecular structures Speech recognition in neural architecture |