Principles of Nonparametric Learning (Paperback, Softcover reprint of the original 1st ed. 2002)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Austria
Series:	CISM International Centre for Mechanical Sciences, 434
Release date:	July 2002
First published:	2002
Editors:	Laszlo Gyoerfi
Dimensions:	244 x 170 x 19mm (L x W x T)
Format:	Paperback
Pages:	335
Edition:	Softcover reprint of the original 1st ed. 2002
ISBN-13:	978-3-211-83688-0
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-211-83688-8
Barcode:	9783211836880

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