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A framework for Similarity-Based Methods (SBMs) includes many classification models as special cases: neural network of the Radial Basis Function Networks type, Feature Space Mapping neurofuzzy networks based on separable transfer functions, Learning Vector Quantization, variants of the k nearest neighbor methods and several new models that may be presented… (More)
Networks estimating probability density are usually based on radial basis function of the same type. Feature Space Mapping constructive network based on separable functions, optimizing type of the function that is added, is described. Small networks of such type may discover accurate representations of data. Numerical experiments on artificial and real… (More)
Application of the neural network methods to problems in physics and chemistry has rapidly gained popularity i n r e c e n t y ears. We show here that for many applications the standard methods of data tting and approximation techniques are much better than neural networks in the sense of giving more accurate results with a lower number of adjustable… (More)
Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons providing decision borders using combinations of soft hyperplanes. The weighted fun-in activation function may be replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model. Non-Euclidean distance… (More)
— 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.
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