Modern multivariate statistical techniques de izenman alan (31 resultados)
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Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Hardcover. Condición: Very Good. Estado de la sobrecubierta: No DJ Issued. 2008 Edition (2008.) Hardcover without dust jacket as issued. 8vo with 731 pages. The book is in very good condition slight bump to one corner. Interior clean and tight, No markings. No online access or CD-ROM or digital access codes if applicable! "a com…pletely new and refreshing approach to statistics and data exploration.comprehensive volume on multivariate statistical analysis. Highly recommended for both Statistics and Computer Science/Electrical Engineering majors." Blue spine/White-Green text. Size: 8vo. Computer Vision & Pattern Reco.
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Hardcover. Condición: Sehr gut. 758 pp Spine slightly discolored, otherwise very well-preserved copy 377 Sprache: Englisch Gewicht in Gramm: 1339.
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Humana 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Taschenbuch. Condición: Neu. Modern Multivariate Statistical Techniques | Regression, Classification, and Manifold Learning | Alan J. Izenman | Taschenbuch | Springer Texts in Statistics | xxv | Englisch | 2016 | Humana | EAN 9781493938322 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidel…berg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Hardcover. Condición: new. Hardcover. 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. Thes…e 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. This book details developments that have led to the introduction of many innovative statistical tools for high-dimensional data analysis. It takes a broad perspective, covering both linear and nonlinear methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Springer 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Editorial: Springer, Humana 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Taschenbuch. Condición: 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.
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Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Condición: New. pp. xxvi + 734.
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Buch. Condición: 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.
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Editorial: Springer Verlag 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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 Ge…nome 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. 760 pp. Englisch.
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Editorial: Springer New York 2016
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Kartoniert / Broschiert. Condición: 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 t…heory, real-data examples from many scientific d.
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Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 760 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Humana Aug 2008 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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 Pr…oject 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. 760 pp. Englisch.
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Editorial: Springer New York 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Gebunden. Condición: 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-dat…a examples from many scientific d.
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Editorial: Springer, Humana Aug 2008 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 Projec…t 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 KG, Sachsenplatz 4-6, 1201 Wien 760 pp. Englisch.
Idioma: Inglés
Editorial: Springer 2008
Serie: Springer Texts in Statistics, Libro 36 de 111. Libro 36 de 111 - Springer Texts in Statistics
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Condición: New. Print on Demand pp. xxvi + 734 Illus.
