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Titlebook: Modern Multivariate Statistical Techniques; Regression, Classifi Alan J. Izenman Textbook 2008 Springer-Verlag New York 2008 Boosting.Clust

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發(fā)表于 2025-3-21 17:19:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Modern Multivariate Statistical Techniques
副標(biāo)題Regression, Classifi
編輯Alan J. Izenman
視頻videohttp://file.papertrans.cn/638/637295/637295.mp4
概述Describes database management systems for maintaining and querying large databases.Provides detailed descriptions of linear and nonlinear data-mining and machine-learning techniques.Integrates theory,
叢書(shū)名稱(chēng)Springer Texts in Statistics
圖書(shū)封面Titlebook: Modern Multivariate Statistical Techniques; Regression, Classifi Alan J. Izenman Textbook 2008 Springer-Verlag New York 2008 Boosting.Clust
描述.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 o
出版日期Textbook 2008
關(guān)鍵詞Boosting; Clustering; Factor analysis; Latent variable model; Linear discriminant analysis; Mathematica; P
版次1
doihttps://doi.org/10.1007/978-0-387-78189-1
isbn_softcover978-1-4939-3832-2
isbn_ebook978-0-387-78189-1Series ISSN 1431-875X Series E-ISSN 2197-4136
issn_series 1431-875X
copyrightSpringer-Verlag New York 2008
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Springer Texts in Statisticshttp://image.papertrans.cn/m/image/637295.jpg
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https://doi.org/10.1007/978-0-387-78189-1Boosting; Clustering; Factor analysis; Latent variable model; Linear discriminant analysis; Mathematica; P
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Textbook 2008nes 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 detai
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1431-875X ar 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 o978-1-4939-3832-2978-0-387-78189-1Series ISSN 1431-875X Series E-ISSN 2197-4136
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Abweichendes Verhalten in Computernetzen,of the group. Its restriction to G/N may be realized as an operator with constant coefficients on A, and its restriction to G/K, which is the Laplacian on G/K, is expressed as a polynomial differential operator on the Borel subgroup AN. Similar results are proved for higher order bi-invariant operat
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