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Bayes sharpening of imprecise information

Piotr KulczyckiMałgorzata Charytanowicz — 2005

International Journal of Applied Mathematics and Computer Science

A complete algorithm is presented for the sharpening of imprecise information, based on the methodology of kernel estimators and the Bayes decision rule, including conditioning factors. The use of the Bayes rule with a nonsymmetrical loss function enables the inclusion of different results of an under- and overestimation of a sharp value (real number), as well as minimizing potential losses. A conditional approach allows to obtain a more precise result thanks to using information entered as the...

A complete gradient clustering algorithm formed with kernel estimators

Piotr KulczyckiMałgorzata Charytanowicz — 2010

International Journal of Applied Mathematics and Computer Science

The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions...

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