Principles of Nonparametric Learning

Laszlo Györfi

2014년 5월 · CISM International Centre for Mechanical Sciences 434권 · Springer

eBook 정보

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.