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.

List of contents

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)

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.

Product details

• Assisted by: Laszlo Györfi (Editor), László Györfi (Editor)
• Publisher: Springer, Wien
• Languages: English
• Product format: Paperback / Softback
• Released: 26.11.2002
• EAN: 9783211836880
• ISBN: 978-3-211-83688-0
• No. of pages: 335
• Dimensions: 170 mm x 243 mm x 18 mm
• Weight: 584 g
• Illustrations: V, 335 p.
• Series: 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
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; B, Statistics, engineering, pattern recognition, 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