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Springer Principles of Nonparametric Learning: 434 (CISM International Centre for Mechanical Sciences, 434)

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Description

This volume provides a 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.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 July 2002
Listed Since
08 December 2006

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