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Series in Statistics: Elements of Statistical Learning : Data Mining, Inference, and Prediction by Trevor Hastie, Jerome Friedman and Robert Tibshirani (2003, Hardcover)
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About this product
Product Identifiers
PublisherSpringer
ISBN-100387952845
ISBN-139780387952840
eBay Product ID (ePID)1941922
Product Key Features
Number of Pages552 Pages
LanguageEnglish
Publication NameElements of Statistical Learning : Data Mining, Inference, and Prediction
SubjectProbability & Statistics / General, Computer Science, Databases / General
Publication Year2003
TypeTextbook
Subject AreaMathematics, Computers
AuthorTrevor Hastie, Jerome Friedman, Robert Tibshirani
SeriesSeries in Statistics
FormatHardcover
Dimensions
Item Weight38.8 Oz
Item Length9.3 in
Item Width6.1 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2001-031433
ReviewsFrom the reviews: SIAM REVIEW "The book is very well written and color is used throughout. Color adds a dimension that can be used to help the reader visualize high-dimensional data, and it is also very useful to help the eye see patterns and clusters more easily. This makes color effective in the book and not just a pleasing gimmick. This is the first book of its kind to treat data mining from a statistical perspective that is comprehensive and up-to-date on the statistical methodsa? I found the book to be both innovative and fresh. It provides an important contribution to data mining and statistical pattern recognition. It should become a classica? It is especially good for statisticians interested in high-dimensional and high-volume data such as can be found in telephone records, satellite images, and genetic microarrays. It can be used for an advanced special topics course in statistics for graduate students." TECHNOMETRICS "[This] is a vast and complex book. Generally, it concentrates on explaining why and how the methods work, rather than how to use them. Examples and especially the visualizations are principle featuresa? As a source for the methods of statistical learninga? it will probably be a long time before there is a competitor to this book." SHORT BOOK REVIEWS "This book describes modern tools for data analysis. With the exception of the last chapter, it is concerned with "supervised" methods - those methods in which a sample of cases is available, including values of an outcome variable, and on which one can build a model allowing one to predict the value of the outcome variable for new cases. The authors are amongst the leaders in this area, having developed many of the modern tools. Such methods have seen extraordinary development in recent decades, primarily because of progress in computer technology, but also because of the huge range of applications. Furthermore, the practical development of these modeling and inferential tools has resulted in a deeper theoretical understanding of the modeling process... The book includes many special cases and examples, which give insights into the ideas and methods. It explains very clearly the relationships between the methods, and covers both standard statistical staples, such as linear and logistic regression, as well as modern tools. It is not overburdened with unnecessary mathematics but uses only what is necessary for the practical application of the methods...The book has been beautifully produced. It was a pleasure to read. I strongly recommend it." MATHEMATICAL REVIEWS "The book provides a comprehensive and up-to-date introduction to the field of statistical pattern recognition, now commonly referred to as statistical learninga? Browsing through the book, one is immediately attracted to the skillful use of color plots to stress the different behaviors of algorithms on real-world datasets. This tells a lot about the books style: intuition about a learning technique is built by looking at the behavior on the data, then the statistical analysis follows. However, even in its most technical parts, the presentation flows very smoothly, avoiding the definition-theorem-proof writing stylea? this is a very complete and up-to-date work covering all the most important learning techniques, which are presented in a rigorous but accessible statistical framework." JOURNAL OF CLASSIFICATION, JUNE 2004 "This is a great book. All three authors have track records for clear exposition and are famously gifted for finding intuitive explanations that illuminate technical resultsa? In particular, we admire the book for its: -outstanding use of real data examples to motivate problems and methods; -unified treatment of flexible inferential procedures in terms of maximization of an objective function subject to a complexity penal
Dewey Edition21
TitleLeadingThe
IllustratedYes
Dewey Decimal006.3/1
SynopsisThis book describes the important ideas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
LC Classification NumberQ325.75.F75 2001
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- Sep 19, 2017
Great book Great product
First of all, this is a great book. Second, this is a great buy, in terms of quality and price.Verified purchase: YesCondition: Pre-owned
