Support vector learning

Foreword The Support Vector Machine has recently been introduced as a new technique for solving various function estimation problems, including the pattern recognition problem. To develop such a technique, it was necessary to rst extract factors responsible for future generalization, to obtain bounds on generalization that depend on these factors, and lastly to develop a technique that constructively minimizes these bounds. The subject of this book are methods based on combining advanced branches of statistics and functional analysis, developing these theories into practical algorithms that perform better than existing heuristic approaches. The book provides a comprehensive analysis of what can be done using Support Vector Machines, achieving record results in real-life pattern recognition problems. In addition, it proposes a new form of nonlinear Principal Component Analysis using Support Vector kernel techniques, which I consider as the most natural and elegant way for generalization of classical Principal Component Analysis. In many ways the Support Vector machine became so popular thanks to works of Bernhard Schh olkopf. The work, submitted for the title of Doktor der Naturwis-senschaften, appears as excellent. It is a substantial contribution to Machine Learning technology.