Modern multivariate statistical techniques: regression, classification, and manifold learning

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 ofdata 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. 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, real-data examples from many scientific disciplines, exercises, and full-color graphics for explaining the various classical and new multivariate statistical techniques INDICE: Preface.- Introduction and preview.- Data and databases.- Random vectors and matrices.- Nonparametric density estimation.- Multiple regression and model assessment.- Multivariate regression.- Linear dimensionality reduction.- Linear discriminant analysis.- Recursive partitioning and decision trees.-Artificial nueral networks.- Support vector machines.- Cluster analysis.- Multidimensional scaling and distance geometry.- Committee machines.- Nonlinear dimensionality reduction.- Wavelets.- Correspondence analysis.- Notation and mathematical results.- References.

• ISBN: 978-0-387-78188-4
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 760
• Fecha Publicación: 01/08/2008
• Nº Volúmenes: 1
• Idioma: Inglés