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.

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)