Principles and theory for data mining and machine learning / (Record no. 4144)

MARC details
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 100316s2009 nyua b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2009930499
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780387981345
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 0387981349
035 ## - SYSTEM CONTROL NUMBER
System control number (Sirsi) u5139
040 ## - CATALOGING SOURCE
Original cataloging agency EG-CaNU
Transcribing agency EG-CaNU
Modifying agency EG-CaNU
042 ## - AUTHENTICATION CODE
Authentication code ncode
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 22
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Clarke, Bertrand.
9 (RLIN) 10264
245 10 - TITLE STATEMENT
Title Principles and theory for data mining and machine learning /
Statement of responsibility, etc. Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang.
250 ## - EDITION STATEMENT
Edition statement 1st ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York :
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. 2009.
300 ## - PHYSICAL DESCRIPTION
Extent xv, 781 p. :
Other physical details ill. ;
Dimensions 24 cm.
490 ## - SERIES STATEMENT
Series statement Springer series in statistics
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Variability, information, prediction.- Kernel smoothing.- Spline smoothing.- New wave nonparametrics.- Supervised learning: Partition methods.- Alternative nonparametrics.- Computational comparisons.- Unsupervised learning: Clustering.- Learning in high dimensions.- Variable selection.- Multiple testing.
520 3# - SUMMARY, ETC.
Summary, etc. This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on nonlinear regression, classification, and ensemble methods. The final chapters focus on clustering, dimension reduction, variable selection, and multiple comparisons. All these topics have undergone extraordinarily rapid development in recent years and this treatment offers a modern perspective emphasizing the most recent contributions. The presentation of foundational results is detailed and includes many accessible proofs not readily available outside original sources. While the orientation is conceptual and theoretical, the main points are regularly reinforced by computational comparisons. Intended primarily as a graduate level textbook for statistics, computer science, and electrical engineering students, this book assumes only a strong foundation in undergraduate statistics and mathematics, and facility with using R packages. The text has a wide variety of problems, many of an exploratory nature.
520 ## - SUMMARY, ETC.
Summary, etc. There are numerous computed examples, complete with code, so that further computations can be carried out readily. The book also serves as a handbook for researchers who want a conceptual overview of the central topics in data mining and machine learning. Bertrand Clarke is a Professor of Statistics in the Department of Medicine, Department of Epidemiology and Public Health, and the Center for Computational Sciences at the University of Miami. He has been on the Editorial Board of the Journal of the American Statistical Association, the Journal of Statistical Planning and Inference, and Statistical Papers. He is co-winner, with Andrew Barron, of the 1990 Browder J. Thompson Prize from the Institute of Electrical and Electronic Engineers. Ernest Fokoue is an Assistant Professor of Statistics at Kettering University. He has also taught at Ohio State University and been a long term visitor at the Statistical and Mathematical Sciences Institute where he was a Post-doctoral Research Fellow in the Data Mining and Machine Learning Program. In 2000, he was the winner of the Young Researcher Award from the International Association for Statistical Computing. Hao Helen Zhang is an Associate Professor of Statistics in the Department of Statistics at North Carolina State University. For 2003-2004, she was a Research Fellow at SAMSI and in 2007, she won a Faculty Early Career Development Award from the National Science Foundation. She is on the Editorial Board of the Journal of the American Statistical Association and Biometrics.
596 ## -
-- 1
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data mining.
9 (RLIN) 10265
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning
General subdivision Statistical methods.
9 (RLIN) 107
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Model uncertainty
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Regularization methods
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term high dimensional and complex data
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term nonlinear methods
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term pattern recognition
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Fokoué, Ernest.
9 (RLIN) 10266
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Zhang, Hao Helen.
9 (RLIN) 5196
920 ## -
-- 9780387981352
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     Main library Main library General Stacks 01/26/2020 AHRA-P   006.31 / CL.P 2009 007149 11/24/2019 1 11/24/2019 Books