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gyorfi l principles nonparametric learning 2002

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Textbook in PDF format The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming. The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions. Pattern Classification and Learning Theory Nonparametric Regression Estimation Universal Prediction Learning-Theoretic Methods in Vector Quantization Distribution and Density Estimation Genetic Programming Applied to Model Identification

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Gyorfi L. Principles of Nonparametric Learning 2002.pdf 20.2 MB

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gyorfi l principles nonparametric learning 2002