The Elements of Statistical Learning: Data Mining, Inference, and PredictionDuring 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 on concepts rather than mathematics. Many examples are given, with a liberal use of color 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 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 a popular 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 the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
From inside the book
Results 1-5 of 37
... Supervised learning ( Machine learning ) . I. Hastie , Trevor . II . Tibshirani , Robert . III . Title . IV . Series . Q325.75 .F75 2001 006.3'1 - dc21 ISBN 978-1-4899-0519-2 ISBN 978-0-387-21606-5 ( eBook ) DOI 10.1007 / 978-0-387 ...
... learning problems that we consider can be roughly categorized as either supervised or unsupervised . In supervised learning , the goal is to pre- dict the value of an outcome measure based on a number of input measures ; in unsupervised ...
... learning , and explain them in a statistical framework . While some mathematical details are needed , we emphasize the methods and their con- ceptual underpinnings rather than their theoretical ... Supervised Learning 2.1 viii Preface.
... Supervised Learning 2.1 Introduction . 2.2 Variable Types and Terminology 2.3 Two Simple Approaches to Prediction ... Learning and Function Approximation 28 2.6.1 A Statistical Model for the Joint Distribution Pr ( X , Y ) 28 2.6.2 ...
... Learning 14.1 Introduction . 14.2 Association Rules . 14.2.1 Market Basket Analysis 14.2.2 The Apriori Algorithm . 14.2.3 Example : Market Basket Analysis 14.2.4 Unsupervised as Supervised Learning 14.2.5 Generalized Association Rules ...
Contents
1 | |
3 | |
5 | |
7 | |
9 | |
11 | |
Bibliographic Notes | 75 |
41 | 108 |
79 | 282 |
Bibliographic Notes | 295 |
Support Vector Machines | 350 |
Bibliographic Notes | 367 |
Flexible Discriminants | 371 |
Bibliographic Notes | 406 |
Prototype Methods and NearestNeighbors | 410 |
Unsupervised Learning | 437 |
55 | 146 |
Bibliographic Notes | 155 |
73 | 159 |
Kernel Methods | 165 |
Additive Models Trees and Related Methods | 257 |
165 | 264 |
Bibliographic Notes | 504 |
81 | 511 |
91 | 517 |
Author Index | 523 |
95 | 530 |