Principles of Nonparametric Learning

Principles of Nonparametric Learning edited by Laszlo Györfi.

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 esti...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Györfi, Laszlo (Editor)
Format: eBook
Language:English
Published: Vienna : Springer Vienna : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:CISM International Centre for Mechanical Sciences, Courses and Lectures, 434
Springer eBook Collection.
Subjects:
Signal processing.
Image processing.
Speech processing systems.
Mathematical statistics.
Pattern recognition.
Statistics .
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary: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.
Physical Description:V, 335 p. online resource.
ISBN:9783709125687
ISSN:0254-1971 ;
DOI:10.1007/978-3-7091-2568-7

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