%0 eBook %M Solr-EB000356640 %A Izenman, Alan J. %I Springer New York %D 2008 %C New York, NY %G English %B Springer Texts in Statistics %@ 9780387781891 %T Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning %U https://doi.org/10.1007/978-0-387-78189-1?nosfx=y %7 1st ed. 2008 %X 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.