The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Trevor Hastie, et al)

 
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Trevor Hastie, et al)

Throughout the past decade, there was an explosion in computation and records generation. With it have come vast amounts of records in a spread of fields along with remedy, biology, finance, and advertising and marketing.

The challenge of understanding these statistics has brought about the improvement of the latest gear in the subject of data and spawned new regions consisting of statistics mining, gadget getting to know, and bioinformatics. a lot of these tools have no unusual underpinnings but are regularly expressed with one-of-a-kind terminology.

This e-book describes the critical ideas in those areas in a commonplace conceptual framework. while the approach is statistical, the emphasis is on principles rather than arithmetic. Many examples are given, with liberal use of coloration portraits. it's miles a treasured useful resource for statisticians and anybody interested in statistics mining in science or enterprise. The e-book's insurance is large, from supervised mastering (prediction) to unsupervised getting to know. the many topics include neural networks, guide vector machines, class trees, and boosting - the primary comprehensive treatment of this subject matter in any e-book.

This predominant new edition capabilities many topics no longer protected in the unique, such as graphical models, random forests, ensemble techniques, least angle regression & direction algorithms for the lasso, non-bad matrix factorization, and spectral clustering. there is additionally a bankruptcy on techniques for ``huge'' statistics (p larger than n), consisting of multiple checking out and fake discovery prices.

Ebook Details

About the Authors
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of records at Stanford University. they are prominent researchers in this vicinity: Hastie and Tibshirani advanced generalized additive fashions and wrote a popular e-book of that title. Hastie co-advanced tons of statistical modeling software and surroundings in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-creator of the very successful An advent to the Bootstrap. Friedman is the co-inventor of much facts-mining equipment including CART, MARS, projection pursuit, and gradient boosting.
Publisher
Published
Published Date / Year
2nd edition (2016); eBook (Online Corrected 12th printing - Jan 13, 2017)
Hardcover
745 pages
eBook Format
PDF (764 pages)
ISBN-10
0387848576
ISBN-13
978-0387848570

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