Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
- First unified presentation of apparently diverse topics in pattern recognition
- Thoroughly class-tested at Berkeley, and at the International Conference on Machine Learning
- Ideal as a graduate textbook, or professional reference/self-teaching
Reviews & endorsements
'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review
'… the book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews
' … I enjoyed reading this book and am happy about is addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is al extremely useful.' IAPR Newsletter
Product details
No date availableAdobe eBook Reader
9780511207006
0 pages
0kg
6 tables
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
- Appendix A: proofs omitted from the main text
- Appendix B: notational conventions
- Appendix C: list of pattern analysis methods
- Appendix D: list of kernels
- References
- Index.
