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Kernel Methods for Pattern Analysis by Nello Cristianini and John Shawe-Taylor (2004, Hardcover)
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Kernel Methods for Pattern Analysis, Hardcover by Shawe-Taylor, John; Cristianini, Nello, ISBN 0521813972, ISBN-13 9780521813976, Brand New, Free shipping in the US 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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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-100521813972
ISBN-139780521813976
eBay Product ID (ePID)2253937
Product Key Features
Number of Pages478 Pages
Publication NameKernel Methods for Pattern Analysis
LanguageEnglish
SubjectCognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Publication Year2004
TypeTextbook
AuthorNello Cristianini, John Shawe-Taylor
Subject AreaComputers, Psychology
FormatHardcover
Dimensions
Item Height1.2 in
Item Weight35.3 Oz
Item Length9.8 in
Item Width6.7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2003-069590
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, 'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. … if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review, '… 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 especailly 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 is 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 al extremely useful.' IAPR Newsletter, "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, "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, 'Kernel methods form an important aspect of modern pattern analysis, and this book gives a lively and timely account of such methods. ... if you want to get a good idea of the current research in this field, this book cannot be ignored.' SIAM Review, '... 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 especailly to those who want to apply kernel-based methods to text analysis and bioinformatics problems.' Computing Reviews
Dewey Edition22
IllustratedYes
Dewey Decimal006.3/1
Table Of ContentPreface; 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.
SynopsisLa philosophie de la religion et la theologie philosophique sont particulierement vivantes dans la philosophie anglo-americaine contemporaine. L'examen des questions les plus classiques, comme la nature et la justification des croyances religieuses, le probleme du mal, la valeur des preuves de l'existence de Dieu, etc., y est direct et argumentatif, comme dans les autres domaines de la philosophie analytique. Premier ouvrage en langue francaise consacre a la philosophie analytique de la religion, ce volume comprend des textes ecrits par des philosophes britanniques et francais. Sans jamais delaisser un traitement au premier degre des questions abordees, certaines etudes consacrees a des auteurs de la tradition contribuent par ailleurs a apporter un eclairage indispensable sur ces questions. Une longue introduction permet au lecteur de s'initier a la philosophie de la raligion de style analytique., 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 provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy 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., 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.
LC Classification NumberQ325.5 .S475 2004
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