The aim of this book is to discuss the fundamental ideas which lie behind the statistical
theory of learning and generalization. It considers learning as a general problem of function
estimation based on empirical data. Omitting proofs and technical details the author
concentrates on discussing the main results of learning theory and their connections to
fundamental problems in statistics. This second edition contains three new chapters devoted to
further development of the learning theory and SVM techniques. Written in a readable and
concise style the book is intended for statisticians mathematicians physicists and computer
scientists.
