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

Description

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 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, 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.

Get the book

Amazon.com

Search the book

eBay.com

Search the book

Other editions

• 

  The Elements of Statistical Learning

  • 2016-12-05
  • 422 pages
  • Paperback
  • Createspace Independent Publishing Platform
• 

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

  • 2013-07-28
  • 536 pages
  • Paperback
  • Springer

Similar books

• 

  An Introduction to Statistical Learning: with Applications in R

  By Daniela Witten, Trevor Hastie, Robert Tibshirani

  This book presents some of the most important modeling and prediction techniques, along with relevant applications.

• 

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

  By Trevor Hastie, Robert Tibshirani, Jerome H. Friedman

  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.

• 

  Statistical Learning with Sparsity: The Lasso and Generalizations

  By Trevor Hastie, Robert Tibshirani, Martin Wainwright

  Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

• 

  The Nature of Statistical Learning Theory

  By Vladimir Vapnik

  The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization.

• 

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

  By R. Tibshirani, J. Friedman

  ; 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.

• 

  All of Statistics: A Concise Course in Statistical Inference

  By Larry Wasserman

  Convergence rates of posterior distributions. The Annals of Statistics 28 500–531. GILKs, W. R., RICHARDSON, S. and SPIEGELHALTER, D. J. (1998). Markov Chain Monte Carlo in Practice. Chapman & Hall. GRIMMETT, G. and STIRZAKER, D.

• 

  Statistical Learning with Math and Python: 100 Exercises for Building Logic

  By Joe Suzuki

  This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.

• 

  Deep Learning

  By Ian Goodfellow, Yoshua Bengio, Aaron Courville

  The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

• 

  Applied Predictive Modeling

  By Max Kuhn, Kjell Johnson

  Castaldi P, Dahabreh I, Ioannidis J (2011). “An Empirical Assessment of Validation Practices for ... Chambers J (2008). Software for Data Analysis: Programming with R. Springer ... Cohen G, Hilario M, Pellegrini C, Geissbuhler A (2005).

• 

  Machine Learning: A Practical Approach on the Statistical Learning Theory

  By RODRIGO F MELLO, Moacir Antonelli Ponti

  This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes.