The Elements of Statistical Learning
Springer series in statisticsPublished 1 January 2009
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
Citations23,408
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on
Keywords
Mathematics
The Annals of StatisticsLeast angle regression
9,493 Citations2004Bradley Efron, Trevor Hastie +2 more
Journal of the American Statistical AssociationRegularized Discriminant Analysis
867 Citations1989Jerome H. Friedman
The Annals of StatisticsPenalized Discriminant Analysis
864 Citations1995Trevor Hastie, Andreas Buja +1 more
A penalized version of Fisher's linear discriminant analysis is described, designed for situations in which there are many highly correlated predictors, such as those obtained by discretizing a function, or the grey-scale values of the pixels in a series of images.
Springer series in statisticsAdditive Models, Trees, and Related Methods
64 Citations2008Trevor Hastie, Robert Tibshirani +1 more
This chapter begins the discussion of some specific methods for supervised learning by describing five related techniques: generalized additive models, trees, multivariate adaptive regression splines, the patient rule induction method, and hierarchical mixtures of experts.
Statisctics and computing/Statistics and computingThe Naive Bayes Classifier
49 Citations2023Matthias Schonlau
Springer series in statisticsSupport Vector Machines and Flexible Discriminants
21 Citations2001Trevor Hastie, J. Friedman +1 more
Springer series in statisticsBasis Expansions and Regularization
19 Citations2001Trevor Hastie, Jerome H. Friedman +1 more
This chapter makes use of models linear in the input features, both for regression and classification, and investigates whether some monotone transformation of Pr(Y = 1|X) is linear in X.
Springer series in statisticsMaximum Likelihood Inference
6 Citations2021Göran Kauermann, Helmut Küchenhoff +1 more
This chapter examines in more detail this estimate as well as the likelihood and its related functions: the log-likelihood, score function and Fisher information, which derives properties of their asymptotic distributions, which tells us how much the functions themselves vary between samples.
Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciencesThe Google PageRank Algorithm
4 Citations2023Jiyu He
Springer series in statisticsPrototype Methods and Nearest-Neighbors
4 Citations2001Trevor Hastie, J. Friedman +1 more