Learning with Support Vector Machines

Yiming Ying

ISBN 10: 3031004248 / ISBN 13: 9783031004247

Published by Springer International Publishing, 2011

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels.

Seller Inventory # 9783031004247

Bibliographic Details

• Title: Learning with Support Vector Machines
• Publisher: Springer International Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3031004248
• ISBN 13: 9783031004247
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 6D millimeters 235 millimeters width by 191 millimeters height by 6 millimeters depth
• Weight: 197 grams

About this title

Synopsis

Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

About the Author

Dr. Colin Campbell holds a BSc degree in physics from Imperial College, London, and a PhD in mathematics from King's College, London. He joined the Faculty of Engineering at the University of Bristol in 1990 where he is currently a Reader. His main interests are in machine learning and algorithm design. Current topics of interest include kernel-based methods, probabilistic graphical models and the application of machine learning techniques to medical decision support and bioinformatics. His research is supported by the EPSRC, Cancer Research UK, the MRC and PASCAL2. Dr. Yiming Ying received his BSc degree in mathematics from Zhejiang University (formally, Hangzhou University) in 1997 and his PhD degree in mathematics from Zhejiang University in 2002, Hangzhou, China. He is currently a Lecturer (Assistant Professor) in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His research interests include machine learning, pattern analysis, convex optimization, probabilistic graphical models and applications to bioinformatics and computer vision.