Kernel methods for pattern analysis / John Shawe-Taylor, Nello Cristianini.

By: Shawe-Taylor, John
Contributor(s): Cristianini, Nello
Language: English Cambridge Cambridge University Press 2011Description: 1 online resource (xiv, 462 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9780511809682Subject(s): Machine learning | Algorithms | Kernel functions | Pattern perception -- Data processingGenre/Form: Electronic books.DDC classification: 006.3 Sh288 2011 Online resources: Full text is available at Cambridge University Press Click here to view
Contents:
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.
Summary: 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.
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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.

000-099

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