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

Kernel methods for pattern analysis /

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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 i...

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Bibliographic Details
Main Author: Shawe-Taylor, John
Other Authors: Cristianini, Nello
Format: Electronic eBook
Language:English
Published: Cambridge, UK ; New York : Cambridge University Press, 2004.
Subjects:
Machine learning.
Algorithms.
Kernel functions.
Pattern perception > Data processing.
Computer algorithms.
Algorithms
Machine Learning
algorithms.
Online Access: Full text (Wentworth users only)
Local Note:ProQuest Ebook Central
Table of Contents:
  • Cover; Half-title; Title; Copyright; Contents; Code fragments; Preface; 1 Pattern analysis; 2 Kernel methods: an overview; 3 Properties of kernels; 4 Detecting stable patterns; 5 Elementary algorithms in feature space; 6 Pattern analysis using eigen-decompositions; 7 Pattern analysis using convex optimisation; 8 Ranking, clustering and data visualisation; 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; A.1 Proof of McDiarmid's theorem.

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