Kernel Methods for Pattern Analysis

by John Shawe-Taylor , Nello Cristianini

• ISBN13: 9780521813976
• ISBN10: 0521813972
• Format: Hardcover
• Copyright: 2004-06-28
• Publisher: Cambridge University Press

Summary

Pattern analysis is the process of finding general relations in a set of data, and forms the core of many disciplines, from neural networks to so-called syntactical pattern recognition, from machine learning to data mining. Applications of pattern analysis range from bioinformatics to document retrieval. The kernel methodology described here provides a powerful and unified framework for all of these disciplines, motivating algorithms that can act on general types of data (e.g. strings, vectors, text etc.) and look for general types of relations (e.g. rankings, classifications, regressions clusters etc.)

Table of Contents

Preface

Part I. Basic Concepts: 1. Pattern analysis

2. Kernel methods: an overview

3. Properties of kernels

4. Detecting stable patterns

Part II. Pattern Analysis Algorithms: 5. Elementary algorithms in feature space

6. Pattern analysis using eigen-decompositions

7. Pattern analysis using convex optimisation

8. Ranking, clustering and data visualisation

Part III. Constructing Kernels: 9. Basic kernels and kernel types

10. Kernels for text

11. Kernels for structured data: strings, trees, etc.

12. Kernels from generative models

Part IV. Appendices

Appendix A. Proof omitted from the main text

Appendix B. Notational conventions

Appendix C. List of pattern analysis methods

Appendix D. List of kernels

Bibliography

Index.