The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition

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Springer Science & Business Media, Aug 26, 2009 - Computers - 767 pages
14 Reviews
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 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.

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Review: The Elements of Statistical Learning: Data Mining, Inference, and Prediction

User Review  - Danial - Goodreads

Unnecessarily dry and difficult to read through; but as a reference book with a solid index it hits its mark. Read full review

Review: The Elements of Statistical Learning: Data Mining, Inference, and Prediction

User Review  - Scott - Goodreads

A classic! One of the first books I read on Machine Learning. Comes at things from the statistics perspective, probably wouldn't recommend as a first introduction. Also would recommend the updated electronic editions (freely available form Hastie's webpage Read full review


Overview of Supervised Learning
Linear Methods for Regression
Linear Methods for Classification
Basis Expansions and Regularization
Kernel Smoothing Methods
Model Assessment and Selection
Model Inference and Averaging
Support Vector Machines and Flexible Discriminants
Prototype Methods and NearestNeighbors
Unsupervised Learning
Random Forests
Ensemble Learning
Undirected Graphical Models
HighDimensional Problems p N

Additive Models Trees and Related Methods
Boosting and Additive Trees
Neural Networks

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About the author (2009)

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.

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