Modern Multivariate Statistical Techniques : (Record no. 6940)

MARC details
000 -LEADER
fixed length control field 02589 a2200241 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250925151539.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241203b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9780387781884
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.535
Item number IzeM
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Izenman, Alan Julian
245 ## - 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. (IMPRINT)
Place of publication New York:
Name of publisher Springer;
Year of publication ©2008
300 ## - PHYSICAL DESCRIPTION
Number of Pages xxv, 733p.
490 ## - SERIES STATEMENT
Series statement Springer Texts in Statistics
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.<br/><br/>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.<br/><br/>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 Statistics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Mathematics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Statistics
General subdivision Multivariate
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Regression
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Multivariate Classification
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Collection code Home library Current library Shelving location Date acquired Source of acquisition Purchase Price Bill number Full call number Accession Number Copy number Print Price Bill Date/Price effective from Koha item type
      Mathematics Indian Institute of Technology Tirupati Indian Institute of Technology Tirupati General Stacks 30/07/2025 Today and Tomorrows Printers and Publishers 8465.65 TTPP/133/2025-26 519.535 IzeM (11533) 11533 Copy 1 12093.79 30/07/2025 Books