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Principles of Nonparametric Learning

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Principles of Nonparametric Learning Synopsis

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.

About This Edition

ISBN: 9783211836880
Publication date:
Author: Laszlo Györfi
Publisher: Springer an imprint of Springer Vienna
Format: Paperback
Pagination: 335 pages
Series: CISM International Centre for Mechanical Sciences
Genres: Electronics engineering
Maths for computer scientists
Pattern recognition
Digital signal processing (DSP)
Probability and statistics