Principles of Nonparametric Learning
Published on 30. July 2002
Book
Paperback/Softback
V, 335 pages
978-3-211-83688-0 (ISBN)
Description
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.
More details
Series
Edition
Softcover reprint of the original 1st ed. 2002
Language
English
Place of publication
Vienna
Austria
Publishing group
Springer Wien
Target group
Professional and scholarly
Research
Illustrations
V, 335 p.
Dimensions
Height: 244 mm
Width: 170 mm
Thickness: 19 mm
Weight
601 gr
ISBN-13
978-3-211-83688-0 (9783211836880)
DOI
10.1007/978-3-7091-2568-7
Schweitzer Classification
Other editions
Additional editions
Laszlo Györfi
Principles of Nonparametric Learning
E-Book
05/2014
Springer
€139.09
Available for download
Content
Pattern classification and learning theory (G. Lugosi).- Nonparametric regression estimation (L. Györfi, M. Kohler).- Universal prediction (N. Cesa-Bianchi).- Learning-theoretic methods in vector quantization (T. Linder).- Distribution and density estimation (L. Devroye, L. Györfi).- Programming applied to model identification (M. Sebag)