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.

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.

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