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Principles and Theory for Data Mining and Machine Learning Springer Series
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A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Item specifics
- Condition
- ISBN
- 9780387981345
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
0387981349
ISBN-13
9780387981345
eBay Product ID (ePID)
72330452
Product Key Features
Number of Pages
Xii, 786 Pages
Publication Name
Principles and Theory for Data Mining and Machine Learning
Language
English
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, Databases / Data Mining
Publication Year
2009
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Series in Statistics Ser.
Format
Hardcover
Dimensions
Item Weight
51.7 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2009-930499
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), 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)
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
Variability, Information, and Prediction.- Local Smoothers.- Spline Smoothing.- New Wave Nonparametrics.- Supervised Learning: Partition Methods.- Alternative Nonparametrics.- Computational Comparisons.- Unsupervised Learning: Clustering.- Learning in High Dimensions.- Variable Selection.- Multiple Testing.
Synopsis
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., This book provides a thorough introduction to the most important topics in data mining and machine learning. All the topics covered have undergone rapid development and this treatment offers a modern perspective emphasizing the most recent contributions.
LC Classification Number
QA76.9.D343
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Seller feedback (1,433)
- l***5 (1497)- Feedback left by buyer.Past monthVerified purchaseFast shipping, arrived as described, smooth transaction, would buy again
- y***i (126)- Feedback left by buyer.Past monthVerified purchaseArrive Friday on time thank you
- 8***j (27)- Feedback left by buyer.Past monthVerified purchaseFast shipping. Shipping arrived on time. Good packaging. Bugs in the bottom of the decorative wood box on the inside. Didn’t want to chance a infestation so unfortunately it went outside in the trash bin. Avon products are good. Doesn’t appear that anything got in the talc. Powder. Get to have my childhood favorite perfume from Avon that got discontinued. Still smells like I remember.