Kernel Methods for Pattern Analysis / Edition 1

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Overview

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.

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Editorial Reviews

From the Publisher
"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 especially 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 its 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 also extremely useful."
IAPR Newsletter

"If you are interested in an introduction to statistical techniques for analyzing text documents, Kernel Methods will serve you well."
M. Last, Journal of the American Statistical Association

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Product Details

  • ISBN-13: 9780521813976
  • Publisher: Cambridge University Press
  • Publication date: 3/1/2003
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 476
  • Product dimensions: 6.85 (w) x 9.72 (h) x 1.14 (d)

Table of 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; 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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