The author of the book: Trevor Hastie
Format files: PDF, EPUB, TXT, DOCX
The size of the: 829 KB
Date of issue: 16 March 2009
Description of the book "The Elements of Statistical Learning":During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is PDF 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. 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 ePub, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote apopular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to PDF the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Reviews of the The Elements of Statistical LearningTo date in regards to the guide we have now The Elements of Statistical Learning comments customers are yet to still eventually left their particular overview of the game, or otherwise read it yet. Nevertheless, should you have already see this e-book and you're wanting to make their discoveries well have you spend your time to exit an overview on our website (we can publish the two negative and positive evaluations). Basically, "freedom of speech" Many of us totally reinforced. Your own responses to book The Elements of Statistical Learning - additional readers will be able to decide of a e-book. This kind of aid will make you far more Joined!
Trevor HastieSadly, presently do not have got information regarding your performer Trevor Hastie. Nevertheless, we'd appreciate should you have almost any specifics of the idea, and they are ready to offer the item. Deliver the idea to all of us! We have all of the examine, of course, if everything tend to be true, we will publish on our website. It is vital for individuals that every correct with regards to Trevor Hastie. We thank you ahead of time internet marketing willing to visit fulfill people!
Download EBOOK The Elements of Statistical Learning for free