Support Vector Machines

• Main Authors: Christmann, Andreas (Author), Steinwart, Ingo (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 2008.
• Series: Information Science and Statistics,
• Subjects: Computer science.; Mathematical statistics.; Data mining.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Probability and Statistics in Computer Science.; Pattern Recognition.; Data Mining and Knowledge Discovery.; Signal, Image and Speech Processing.

This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify thre...

Table of Contents:

• Loss Functions and Their Risks
• Surrogate Loss Functions (*)
• Kernels and Reproducing Kernel Hilbert Spaces
• Infinite-Sample Versions of Support VectorMachines
• Basic Statistical Analysis of SVMs
• Advanced Statistical Analysis of SVMs (*)
• Support Vector Machines for Classification
• Support Vector Machines for Regression.
• Robustness
• Computational Aspects
• Data Mining.