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. 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 .

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zlib/Computers/Computer Science/Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang (auth.)/Principles and Theory for Data Mining and Machine Learning_1057365.pdf

Clarke, Bertrand, Fokoue, Ernest, Zhang, Hao Helen

by Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang

Bertrand S Clarke

Copernicus

Telos

Springer Series in Statistics, New York, NY, New York State, 2009

Springer series in statistics, Dordrecht, 2009

Springer series in statistics, Berlin, ©2009

United States, United States of America

Springer Nature, Dordrecht, 2009

2009, FR, 2009

до 2011-08

sm21765389

MiU

The idea for this book came from the time the authors spent at the Statistics and Applied Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina starting in fall 2003. The rst author was there for a total of two years, the rst year as a Duke/SAMSI Research Fellow. The second author was there for a year as a Post-Doctoral Scholar. The third author has the great fortune to be in RTP p- manently. SAMSI was – and remains – an incredibly rich intellectual environment with a general atmosphere of free-wheeling inquiry that cuts across established elds. SAMSI encourages creativity: It is the kind of place where researchers can be found at work in the small hours of the morning – computing, interpreting computations, and developing methodology. Visiting SAMSI is a unique and wonderful experience. The people most responsible for making SAMSI the great success it is include Jim Berger, Alan Karr, and Steve Marron. We would also like to express our gratitude to Dalene Stangl and all the others from Duke, UNC-Chapel Hill, and NC State, as well as to the visitors (short and long term) who were involved in the SAMSI programs. It was a magical time we remember with ongoing appreciation.

Front Matter....Pages i-xiv
Variability, Information, and Prediction....Pages 1-52
Local Smoothers....Pages 53-116
Spline Smoothing....Pages 117-170
New Wave Nonparametrics....Pages 171-230
Supervised Learning: Partition Methods....Pages 231-306
Alternative Nonparametrics....Pages 307-363
Computational Comparisons....Pages 365-404
Unsupervised Learning: Clustering....Pages 405-491
Learning in High Dimensions....Pages 493-568
Variable Selection....Pages 569-678
Multiple Testing....Pages 679-742
Back Matter....Pages 1-38

Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Offers a math level treatment of the basic techniques that are on the interface of Stats and Compsci

2011-08-31