E-Book, Englisch, Band 434, 335 Seiten, eBook
Györfi Principles of Nonparametric Learning
Erscheinungsjahr 2014
ISBN: 978-3-7091-2568-7
Verlag: Springer Wien
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 434, 335 Seiten, eBook
Reihe: CISM International Centre for Mechanical Sciences
ISBN: 978-3-7091-2568-7
Verlag: Springer Wien
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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)
