Principles of Nonparametric Learning
(Sprache: Englisch)
This volume provides a 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,...
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Klappentext zu „Principles of Nonparametric Learning “
This volume provides a 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.
Inhaltsverzeichnis zu „Principles of Nonparametric Learning “
- 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)
Bibliographische Angaben
- 2002, 2002., 335 Seiten, Maße: 24,4 cm, Kartoniert (TB), Englisch
- Herausgegeben:Györfi, Laszlo
- Verlag: Springer
- ISBN-10: 3211836888
- ISBN-13: 9783211836880
Sprache:
Englisch
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