Principles of Nonparametric Learning

Buch | Softcover

V, 335 Seiten

2002 | 2002
Springer Wien (Verlag)
978-3-211-83688-0 (ISBN)

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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.

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)

Erscheint lt. Verlag	30.7.2002
Reihe/Serie	CISM International Centre for Mechanical Sciences
Zusatzinfo	V, 335 p.
Verlagsort	Vienna
Sprache	englisch
Maße	170 x 244 mm
Gewicht	582 g
Themenwelt	Technik ► Elektrotechnik / Energietechnik
Schlagworte	algorithms • classification • Cognition • Complexity • Computer Science • genetic programming • grammar • Kernel • learning • Mustererkennung • Pattern • pattern recognition • Probability • Probability and Statistics in Computer Science • programming • Regression (Statistik) • Signal Processing • Statistical Theory and Methods • Statistics • Statistik • Wahrscheinlichkeit
ISBN-10	3-211-83688-8 / 3211836888
ISBN-13	978-3-211-83688-0 / 9783211836880
Zustand	Neuware