Modern multivariate statistical techniques : (Record no. 15395)

MARC details
000 -LEADER
fixed length control field 03255nam a2200229Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field NULRC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250520102805.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250520s9999 xx 000 0 und d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780387781884
040 ## - CATALOGING SOURCE
Transcribing agency NULRC
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA 278 .I94 2013
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Izenman, Alan Julian
Relator term author
245 #0 - TITLE STATEMENT
Title Modern multivariate statistical techniques :
Remainder of title regression, classification, and manifold learning /
Statement of responsibility, etc. Alan Julian Izenman
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York :
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. c2013
300 ## - PHYSICAL DESCRIPTION
Extent xxv, 733 pages :
Other physical details color illustrations ;
Dimensions 24 cm.
365 ## - TRADE PRICE
Price amount USD59.33
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introduction and preview -- 2. Data and databases -- 3. Random vectors and matrices -- 4. Nonparametric density estimation -- 5. Model assessment and selection in multiple regression -- 6. Multivariate regression -- 7. Linear dimensionality reduction -- 8. Linear discriminant analysis -- 9. Recursive partitioning and tree-based methods -- 10. Artificial neural networks -- 11. Support vector machines -- 12. Cluster analysis -- 13. Multidimensional scaling and distance geometry -- 14. Committee machines -- 15. Latent variable models for blind source separation -- 16. Nonlinear dimensionality reduction and manifold learning -- 17. Correspondence analysis.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MULTIVARIATE ANALYSIS
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Library of Congress Classification     Gen. Ed - CEAS LRC - Graduate Studies National University - Manila General Circulation 10/06/2016 Purchased - Amazon 178.00   GC QA 278 .I94 2013 NULIB000013154 05/20/2025 c.1 05/20/2025 Books