Shawe-Taylor, John.

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.



9780511809682


Machine learning.
Algorithms.
Kernel functions.
Pattern perception--Data processing.


Electronic books.

006.3 Sh288 2011