The Elements Of Statistical Learning by T Hastie

Description: About this productSynopsisDuring the past decade there has been an explosion in computation and information technology. With it has 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 descibes theimprtant ideas in these areas ina 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 vluable resource for statisticians and anyone interested in data mining in science or industry.The book's coverage is broad, from supervised learing (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.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 a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.The visionary creator of the Academy Award-winning Pan's Labyrinth and a Hammett Award-winning author bring their imaginations to this bold, epic novel about a horrifying battle between man and vampire that threatens all humanity. It is the first installment in a thrilling trilogy and an extraordinary international publishing event. The Strain They have always been here. Vampires. In secret and in darkness. Waiting. Now their time has come. In one week, Manhattan will be gone. In one month, the country. In two monthsthe world. A Boeing 777 arrives at JFK and is on its way across the tarmac, when it suddenly stops dead. All window shades are pulled down. All lights are out. All communication channels have gone quiet. Crews on the ground are lost for answers, but an alert goes out to the CDC. Dr. Eph Goodweather, head of their Canary project, a rapid-response team that investigates biological threats, gets the call and boards the plane. What he finds makes his blood run cold. In a pawnshop in Spanish Harlem, a former professor and survivor of the Holocaust named Abraham Setrakian knows something is happening. And he knows the time has come, that a war is brewing . . . So begins a battle of mammoth proportions as the vampiric virus that has infected New York begins to spill out into the streets. Eph, who is joined by Setrakian and a motley crew of fighters, must now find a way to stop the contagion and save his citya city that includes his wife and sonbefore it is too late.This 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.Product IdentifiersISBN-100387952845ISBN-139780387952840eBay Product ID (ePID)1941922Key DetailsAuthorJerome Friedman, Robert Tibshirani, Trevor HastieNumber Of Pages552 pagesSeriesSeries in StatisticsFormatHardcoverPublication Date2003-07-30LanguageEnglishPublisherSpringerPublication Year2003Additional DetailsCopyright Date2003IllustratedYesDimensionsWeight38.8 OzWidth6.1 In.Length9.3 In.Target AudienceGroupScholarly & ProfessionalClassification MethodLCCN2001-031433LC Classification NumberQ325.75.F75 2001Dewey Decimal006.3/1Dewey Edition21Table Of ContentIntroduction * Overview of Supervised Larnings * Linear Methods for Regression * Linear Methods for Classification * Basic Expansions and Regularization * Kernel Methods * Model Assessment and Selection * Model Inference and Averaging * Additive Models, Trees, and Related Methods * Boosting and Additive Trees * Neural Networks * Support Vector Machines and Flexible Discriminants * Prototype Methods and Nearest Neighbors * Unsupervised LearningReviewsFrom 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

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