Table of contents
About this book
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 Recognition Probability and Statistics in Computer Science Signal Processing Statistical Theory and Methods algorithms classification cognition complexity computer science genetic programming grammar kernel learning pattern pattern recognition probability programming signal processing statistics