The Elements of Statistical Learning The Elements of Statistical Learning
Springer Series in Statistics

The Elements of Statistical Learning

Data Mining, Inference, and Prediction, Second Edition

Trevor Hastie and Others
    • 5.0 • 3 Ratings
    • $69.99
    • $69.99

Publisher 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 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 and 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.

GENRE
Computers & Internet
RELEASED
2009
August 26
LANGUAGE
EN
English
LENGTH
767
Pages
PUBLISHER
Springer New York
SELLER
Springer Nature B.V.
SIZE
13.7
MB
Deep Learning Deep Learning
2016
Understanding Deep Learning Understanding Deep Learning
2023
The Hundred-Page Machine Learning Book The Hundred-Page Machine Learning Book
2019
Practical Statistics for Data Scientists Practical Statistics for Data Scientists
2020
The Book of R The Book of R
2016
500 Data Science Interview Questions and Answers 500 Data Science Interview Questions and Answers
2020
An Introduction to Statistical Learning An Introduction to Statistical Learning
2013
Computer Age Statistical Inference Computer Age Statistical Inference
2016
Deep Learning Deep Learning
2016
Bayesian Statistics the Fun Way Bayesian Statistics the Fun Way
2019
Designing Data-Intensive Applications Designing Data-Intensive Applications
2017
The Unicorn Project The Unicorn Project
2019
Automate the Boring Stuff with Python, 2nd Edition Automate the Boring Stuff with Python, 2nd Edition
2015
The Phoenix Project The Phoenix Project
2018
Regression Modeling Strategies Regression Modeling Strategies
2015
Forecasting with Exponential Smoothing Forecasting with Exponential Smoothing
2008
An Introduction to Sequential Monte Carlo An Introduction to Sequential Monte Carlo
2020
Simulation and Inference for Stochastic Differential Equations Simulation and Inference for Stochastic Differential Equations
2009
Statistics for High-Dimensional Data Statistics for High-Dimensional Data
2011
Targeted Learning in Data Science Targeted Learning in Data Science
2018