The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.

$16.99

10 in stock

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Secure Payments

Pay with the worlds payment methods.

Discount Available

Covers payment and purchase gifts.

100% Money-Back Guarantee

Need Help?

(484) 414-5835

Share Our Wines With Your Friends & Family

Description

Editorial Reviews

Review

The reviews were from.

The current edition is equally welcome to researchers and academicians. Most of the chapters have been revised. The general layout of the Material is the same as it was in the first edition. If you bought the first edition, I recommend you buy the second edition for maximum effect, and if you haven’t, I still recommend you have this book at your desk. Is it a good investment? The book review editor. It’s called technometrics. August 2009, VOL. 51, No.

The second edition was reviewed.

This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters were included. This book is worth getting because of the additions. This book gives a good overview of statistical learning and can be recommended to anyone who is interested in this field. There is material for several courses in the book. The International Statistical Review was written by Klaus Nordhausen. 77 (3), 2009,

The second edition has about 200 pages of new additions in the form of four new chapters, as well as various complement to existing chapters. The book is of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. An already fine book will surely reinforce its status as a reference as a result of this update. The issue of Mathematical Reviews was published in 2012 d.

The book is ideal for statistics graduate students. This book is the standard in the field and it is easy to see why. The book is well written and informative. It looks great. You can flip the book open, read a sentence or two, and be hooked for the next hour or so. Peter Rabinovitch is a member of The Mathematical Association of America.

–This text refers to an alternate kindle_edition edition.

About the Author

The professors of statistics are at the university. They are prominent researchers in this area and wrote a popular book about their work. The principal curves and surfaces were invented by Hastie. An introduction to the bootstrap was co-authored by the authors of the lasso. The co-inventor of many data-mining tools is Friedman.

–This text refers to an alternate kindle_edition edition.

From the Back Cover

The past decade has seen an explosion in computation and information technology. There is a lot of data in medicine, biology, finance, and marketing. The development of new tools in the field of statistics has been the result of the challenge of understanding the data. Many of the tools have the same foundation but are often expressed in different ways. The book describes important ideas in a conceptual framework. The emphasis is not on mathematics but on concepts. Many examples have a liberal use of color graphics. Anyone interested in data mining in science or industry can use it. The book’s coverage is broad. The first comprehensive treatment of neural networks, support vector machines, classification trees and boosting is in the book.

There are many topics not covered in the original that are included in the new edition. There is a chapter on methods for wide data. p It’s bigger than that. n Multiple testing and false discovery rates are included.

The professors of statistics are at the university. They are prominent researchers in this area and wrote a popular book about their work. The principal curves and surfaces were invented by Hastie. The very successful lasso was co-authored by the two people who proposed it. There is an introduction to the bootstrap. . The co-inventor of many data-mining tools is Friedman.

–This text refers to an alternate kindle_edition edition.

Read more

Additional information

Best Sellers Rank

#472,762 in Kindle Store (See Top 100 in Kindle Store) #22 in Bioinformatics (Kindle Store) #123 in Data Mining (Kindle Store) #132 in Bioinformatics (Books)
Customer Reviews

/* * Fix for UDP-1061. Average customer reviews has a small extra line on hover * https

Reviews

There are no reviews yet.

Be the first to review “The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)”

Your email address will not be published. Required fields are marked *

OUR BEST COLLECTION OF COURSES AND BOOKS

Hot Popular Books/Courses ?

365 Kearny St, CA, 94105
support@nextskillup.com
Phone: (850) 312-7610
DMCA.com Protection Status

Policies

Useful links

Newsletter

Copyright © 2025 nextskillup.com – All Rights Reserved.