Kernel methods for pattern analysis /
John Shawe-Taylor, Nello Cristianini.
- 1 online resource (xiv, 462 pages) : illustrations ;
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.
The 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.