Minimal distance neural methods . W lodzis
نویسنده
چکیده
A general framework for minimal distance methods is presented. Radial Basis Functions (RBFs) and Multilayer Perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on a real-world datasets obtaining very encouraging results.
منابع مشابه
Neural minimal distance methods
Minimal distance methods are simple and in some circumstances highly accurate. In this paper relations between neural and minimal distance methods are investigated. Neural realization facilitates new versions of minimal distance methods. Parametrization of distance functions, distance-based weighting of neighbors, active selection of reference vectors from the training set and relations to the ...
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