Cited by 25,970Statistical Methods and ApplicationsStatistical Methods and InferenceMachine Learning and Algorithms

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The Elements of Statistical Learning

Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome·2009·Springer Series in Statistics

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Abstract

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

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Crossref · 10.1007/978-0-387-84858-7

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How to measure uncertainty in uncertainty sampling for active learning
Nguyen, V.-L., Shaker, M., & Hüllermeier, E. (2021) · Machine Learning · doi:10.1007/s10994-021-06003-9 · Cited by 198

Authors: Hastie, T., Tibshirani, R., & Friedman, J.
Affiliations: Stanford University (US)
ORCID: Hastie, Trevor
Tibshirani, Robert
Friedman, Jerome
Venue: Springer Series in Statistics
Publisher: Springer New York
Published: 2009
ISSN: 0172-7397 (ISSN-L)
2197-568X
DOI: 10.1007/978-0-387-84858-7
OpenAlex: W2787894218
Cited by: 25,970
References: 0
Citations by year: 2026: 3306 · 2025: 3252 · 2024: 2901 · 2023: 2360 · 2022: 2154 · 2021: 2134 · 2020: 1664 · 2019: 1391 · 2018: 1170 · 2017: 1082 · 2016: 1024 · 2015: 1009 · 2014: 725 · 2013: 623 · 2012: 513
FWCI: 61.16
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Springer series in statistics (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords: statistical learning, machine learning, statistical inference, data analysis, applied statistics

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