Includes bibliographical references (pages 450-459) and index.
CONTENTS NOTE
Text of Note
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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SUMMARY OR ABSTRACT
Text of Note
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.
OTHER EDITION IN ANOTHER MEDIUM
Title
Kernel methods for pattern analysis.
International Standard Book Number
0521813972
TOPICAL NAME USED AS SUBJECT
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.