Titlebook: New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing; Leszek Rutkowski Book 2004 Springer-Verlag

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Generalized Regression Neural Networks in a Time-Varying Environment], [138], [158], [190], [192], [209] — [214], [218] — [221], [223], [226] — [229], [261] — [263]) who created non-parametric algorithms based on the Parzen method, the orthogonal series or the nearest neighbor methods.

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Probabilistic Neural Networks for Pattern Classification in a Time-Varying Environmentof dynamic stochastic approximation algorithms. Of course, such an approach does not ensure an asymptotic optimality of the classification rules. Rutkowski [217] considered the asymptotically optimal classification rules in a quasi-stationary environment when class conditional densities are converge

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1434-9922 ng, integrating various disciplines from the fields of information science and engineering. The book consists of three parts, the first of which is devoted to probabilistic neural net­ works. Neural978-3-642-05820-2978-3-540-40046-2Series ISSN 1434-9922 Series E-ISSN 1860-0808

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Vector Quantization for Image Compressionmay not be detected at all. In this chapter we first explain how to prepare an image for vector quantization. Next the image compression problem is explained and the VQ algorithm based on neural networks is described.

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