Ebook: Principles of Nonparametric Learning
Author: László Györfi (eds.)
- Tags: Signal Image and Speech Processing, Probability and Statistics in Computer Science, Pattern Recognition, Statistical Theory and Methods
- Series: International Centre for Mechanical Sciences 434
- Year: 2002
- Publisher: Springer-Verlag Wien
- Edition: 1
- Language: English
- pdf
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.
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.
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