Description
Book SynopsisThe kernel functions methodology described here provides a powerful and unified framework for disciplines ranging from neural networks and pattern recognition to machine learning and data mining. This book provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, suitable for standard pattern discovery problems.
Trade Review'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
Table of ContentsPreface; 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.
