Authors:
This is a more theoretical book on the same subject as the book on statistical learning by Hastie/Tibshirani/Friedman.
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Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
About this book
Keywords
- Clustering
- classification
- data mining
- linear regression
- machine learning
- supervised learning
- unsupervised learning
Reviews
From the reviews:
“PhD level students, and researchers and practitioners in statistical learning and machine learning. … text assumes a thorough training in undergraduate statistics and mathematics. Computed examples that include R code are scattered through the text. There are numerous exercises, many with commentary that sets out guidelines for exploration. … The over-riding reason for staying with the independent, symmetric unimodal error model is surely that no one book can cover everything! Within these bounds, this book gives a careful treatment that is encyclopedic in its scope.” (John H. Maindonald, International Statistical Review, Vol. 79 (1), 2011)
“It is an appropriate textbook for a PhD level course and can also be used as a reference or for independent reading. … an excellent resource for researchers and students interested in DMML. … the authors have done an outstanding job of covering important topics and providing relevant statistical theory and computational resources. I can see myself teaching a statistical learning class using this book and comfortably recommend it to any researcher with a solid mathematical background who wants to be engaged in this field.” (Jeongyoun Ahn, Journal of the American Statistical Association, Vol. 106 (493), March, 2011)
“This book provides an encyclopedic monograph on this field from a statistical point of view. … A salient feature of this book is its coverage of theoretical aspects of DMML techniques. … Additionally, plenty of exercises and computational examples with R codes are provided to help one brush up on the technical content of the text.” (Kazuho Watanabe, Mathematical Reviews, Issue 2012 i)
Authors and Affiliations
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Dept. Statistics, University of British Columbia, Vancouver, Canada
Bertrand Clarke
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Dept. Science & Mathematics, Kettering University, Flint, U.S.A.
Ernest Fokoue
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Dept. Statistics, North Carolina State University, Raleigh, U.S.A.
Hao Helen Zhang
Bibliographic Information
Book Title: Principles and Theory for Data Mining and Machine Learning
Authors: Bertrand Clarke, Ernest Fokoue, Hao Helen Zhang
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-0-387-98135-2
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag New York 2009
Hardcover ISBN: 978-0-387-98134-5Published: 30 July 2009
Softcover ISBN: 978-1-4614-1707-1Published: 02 December 2011
eBook ISBN: 978-0-387-98135-2Published: 21 July 2009
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XII, 786
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence, Probability Theory, Statistical Theory and Methods, Computational and Systems Biology, Automated Pattern Recognition