Learning with Support Vector Machines

✍ Scribed by Colin Campbell, Yiming Ying

• Publisher: Springer
• Year: 2011
• Tongue: English
• Leaves: 91
• Series: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Edition: 1
• Category: Library

✦ 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

✦ Table of Contents

Cover
Copyright Page
Title Page
Contents
Preface
Acknowledgments
Support Vector Machines for Classification
Introduction
Support Vector Machines for binary classification
Multi-class classification
Learning with noise: soft margins
Algorithmic implementation of Support Vector Machines
Case Study 1: training a Support Vector Machine
Case Study 2: predicting disease progression
Case Study 3: drug discovery through active learning
Kernel-based Models
Introduction
Other kernel-based learning machines
Introducing a confidence measure
One class classification
Regression: learning with real-valued labels
Structured output learning
Learning with Kernels
Introduction
Properties of kernels
Simple kernels
Kernels for strings and sequences
Kernels for graphs
Multiple kernel learning
Learning kernel combinations via a maximum margin approach
Algorithmic approaches to multiple kernel learning
Case Study 4: protein fold prediction
Appendix
Introduction to optimization theory
Duality
Constrained optimization
Bibliography
Authors' Biography