Includes bibliographical references (pages 450-459) and index.
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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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.
Kernel methods for pattern analysis.
0521813972
Algorithms.
Kernel functions.
Machine learning.
Pattern perception-- Data processing.
Algorithmes.
Apprentissage automatique.
Noyaux (Mathématiques)
Perception des structures-- Informatique.
Algorithms.
Algoritmen.
COMPUTERS-- Enterprise Applications-- Business Intelligence Tools.