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|a Shawe-Taylor, John.
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|a Kernel methods for pattern analysis /
|c John Shawe-Taylor, Nello Cristianini.
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|a Cambridge, UK ;
|a New York :
|b Cambridge University Press,
|c 2004.
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|a 1 online resource (xiv, 462 pages) :
|b illustrations
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|a text
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|a Includes bibliographical references (pages 450-459) and index.
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|a 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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|a 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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|a Print version record.
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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650 |
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|a Machine learning.
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650 |
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|a Algorithms.
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650 |
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|a Kernel functions.
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650 |
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|a Pattern perception
|x Data processing.
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2 |
|a Algorithms
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650 |
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2 |
|a Machine Learning
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|a Apprentissage automatique.
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650 |
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6 |
|a Algorithmes.
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650 |
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|a Noyaux (Mathématiques)
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|2 aat
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|a Computer algorithms
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|a Algorithms
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|a Kernel functions
|2 fast
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|a Machine learning
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|
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|a Pattern perception
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|a Patroonherkenning.
|2 gtt
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1 |
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|a Algoritmen.
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700 |
1 |
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|a Cristianini, Nello.
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758 |
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|i has work:
|a Kernel methods for pattern analysis (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGP4fXW7MjKymFyF4fJ8kC
|4 https://id.oclc.org/worldcat/ontology/hasWork
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776 |
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|i Print version:
|a Shawe-Taylor, John.
|t Kernel methods for pattern analysis.
|d Cambridge, UK ; New York : Cambridge University Press, 2004
|z 0521813972
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