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This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics, text analysis and image analysis. It also serves as an 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.Read more
- 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
"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 especially to those who want to apply kernel-based methods to text analysis and bioinformatics problems."
Computing ReviewsSee more reviews
"I enjoyed reading this book and am happy about its 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 also extremely useful."
"If you are interested in an introduction to statistical techniques for analyzing text documents, Kernel Methods will serve you well."
M. Last, Journal of the American Statistical Association
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- Date Published: June 2004
- format: Hardback
- isbn: 9780521813976
- length: 478 pages
- dimensions: 249 x 170 x 30 mm
- weight: 1kg
- contains: 6 tables
- availability: Available
Table of Contents
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
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