Principles of Nonparametric Learning

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.

Sommario

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)

Riassunto

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.

Dettagli sul prodotto

• Con la collaborazione di: Laszlo Györfi (Editore), László Györfi (Editore)
• Editore: Springer, Wien
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 26.11.2002
• EAN: 9783211836880
• ISBN: 978-3-211-83688-0
• Pagine: 335
• Dimensioni: 170 mm x 243 mm x 18 mm
• Peso: 584 g
• Illustrazioni: V, 335 p.
• Serie: CISM International Centre for Mechanical Sciences; CISM Courses and Lectures, International Centre for Mechanical Sciences; CISM International Centre for Mechanical Sciences; CISM Courses and Lectures, International Centre for Mechanical Sciences
• Categorie: Scienze naturali, medicina, informatica, tecnica > Tecnica > Elettronica, elettrotecnica, telecomunicazioni; B, Statistics, Mustererkennung, Wahrscheinlichkeitsrechnung und Statistik, Mathematik für Informatiker, engineering, pattern recognition, Digitale Signalverarbeitung (DSP), Signal, Image and Speech Processing, Statistical Theory and Methods, Signal Processing, Probability & statistics, Mathematical & statistical software, Automated Pattern Recognition, Maths for computer scientists, Mathematical statistics, Probability and Statistics in Computer Science, Speech processing systems, Image processing, Signal, Speech and Image Processing