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

By W. J. Braun, Duncan J. Murdoch, M. Hlynka, Stefano Maria Iacus, Anthony B. Atkinson, Aleksandar Donev, Randall D. Tobias, B. C. Arnold, Narayanaswamy Balakrishnan, E. Schilling and 3 more. 2010

2,135 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Machine Learning that Matters (2012, background): “The "machine learning contribution" It is recognized that the performance obtained by training a model M on data set X may not reﬂect M ’s performance on other data sets drawn from the same problem, i.e., training loss is an underestimate of test loss (Hastie et al., 2001).”
Distributed optimization of deeply nested systems (2012, methodology): “In all cases, we show the speedup achieved with a parallel implementation of MAC as well.”
Solar Forcing for CMIP6 (v3.1) (2016, methodology): “20 This independence of the intervals on which the model is trained and then tested (called cross-validation (Hastie et al., 2009)) is essential for it and allows to test the performance of the model and define confidence intervals that truly reflect the difference between the constructed and actual”
A researcher's guide to regression, discretization, and median splits of continuous variables (2015, methodology): “For more details, we refer the interested reader to texts such as Bates and Watts (1988), Hastie, Tibshirani, and Friedman (2009), James, Witten, Hastie, and Tibshirani (2013), Lindsey (2001), McCullagh and Nelder (1989), and Seber and Wild (2003).”
Distributed Constrained Optimization by Consensus-Based Primal-Dual Perturbation Method (2013, methodology): “In addition to the smart grid applications, problem (2) incorporates the important regression problems which widely appear in control [5], machine learning [6], [7], data mining [32], [33] and imaging processing [7] applications.”
Joint analysis of functional genomic data and genome-wide association studies of 18 human traits (2013, methodology): “The penalty p on the sum of the squared annotation parameters is the one used in ridge regression.(33) In ridge regression, parameter estimates under this penalty are equivalent to the posterior means of the parameter if the prior distribution of the parameter is Gaussian;(33) changing the tuning pa”
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “Different categorizations of the ML algorithms can be found [34]–[36].”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “The extracted items included general study characteristics, applied algorithms and their characteristics, data-driven variable selection, and model performance (Table A.1, Appendix B) [1,2,13,23 e 25].”
Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest (2016, methodology): “Numerical experiments show that increasing the fraction of relevant features can improve the performance of the random forest model by increasing the chance that important features will be selected at each tree splitting.([44]) In this work, by taking advantage of random forest in ranking features, ”
How accurately can one predict drug binding modes using AlphaFold models? (2023, methodology): “An Epanech-nikov kernel ( Hastie et al., 2009 ) was used, with a width of 0.25 Å.”