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.
| ISBN: | 9783211836880 |
| Publication date: | 30th July 2002 |
| Author: | Laszlo Györfi |
| Publisher: | Springer an imprint of Springer Vienna |
| Format: | Paperback |
| Pagination: | 335 pages |
| Series: | CISM International Centre for Mechanical Sciences |
| Genres: |
Electronics engineering Maths for computer scientists Pattern recognition Digital signal processing (DSP) Probability and statistics |
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.
Principles of Nonparametric Learning features in the following genres: Electronics engineering, Maths for computer scientists, Pattern recognition, Digital signal processing (DSP), Probability and statistics
Paperback. Not Available.
Principles of Nonparametric Learning was written by Laszlo Györfi and published by Springer an imprint of Springer Vienna
Principles of Nonparametric Learning has 335 pages
Yes it is part of CISM International Centre for Mechanical Sciences series