Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning - Couverture souple

Izenman, Alan J.

9781493938322: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning

Couverture souple

ISBN 10 : 1493938320 ISBN 13 : 9781493938322

Editeur : Springer-Verlag New York Inc., 2016

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Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.

These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.

This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.

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�diteur: Springer-Verlag New York Inc.
Date d'�dition: 2016
Langue: anglais
ISBN 10: 1493938320
ISBN 13: 9781493938322
Reliure: Broch�
Nombre de pages: 733
Coordonn�es du fabricant: non disponible

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9780387781884: Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning

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ISBN 10 : 0387781889 ISBN 13 : 9780387781884
Editeur : Springer-Verlag New York Inc., 2008
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Modern Multivariate Statistical Techniques

Alan J. Izenman

Edit� par Springer New York, 2016

ISBN 10 : 1493938320 ISBN 13 : 9781493938322

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Describes database management systems for maintaining and querying large databasesProvides detailed descriptions of linear and nonlinear data-mining and machine-learning techniquesIntegrates theory, real-data examples from many scientific d. N� de r�f. du vendeur 274518045

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Modern Multivariate Statistical Techniques

Alan J. Izenman

Edit� par Springer New York Aug 2016, 2016

ISBN 10 : 1493938320 ISBN 13 : 9781493938322

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before. 760 pp. Englisch. N� de r�f. du vendeur 9781493938322

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Modern Multivariate Statistical Techniques

Alan J. Izenman

Edit� par Springer New York, Springer New York Aug 2016, 2016

ISBN 10 : 1493938320 ISBN 13 : 9781493938322

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Taschenbuch. Etat : Neu. Neuware -Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 760 pp. Englisch. N� de r�f. du vendeur 9781493938322

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Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning

Alan J. Izenman

Edit� par Springer New York, Springer New York, 2016

ISBN 10 : 1493938320 ISBN 13 : 9781493938322

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics. These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs. N� de r�f. du vendeur 9781493938322

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