The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman

By Klaus Nordhausen. 2009

2,835 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, methodology): “A third approach capitalizes on association rules, popular in marketing studies (Hastie et al., 2009: section 14.2).”
Fast and accurate modeling of molecular atomization energies with machine learning. (2011, methodology): “We used stratified [30] fivefold cross validation [28,29] for model selection and to estimate performance.”
Accelerating the Convergence Rates of Distributed Subgradient Methods with Adaptive Quantization (2018, methodology): “5 Simulations In this section, we apply Algorithm 1 for solving linear regression problems, the most popular technique for data fitting [10, 31] in statistical machine learning, over a network of processors under random quantization.”
A data-driven approach to predict the success of bank telemarketing (2014, methodology): “[13], to solve this issue, the rminer package uses an ensemble of Nr different trained networks and outputs the average of the individual predictions [13].”
Bypassing the Kohn-Sham equations with machine learning (2016, methodology): “This approach models the energy directly as a functional of v ( r ), i.e. it trains a model via KRR (for details, see supplement).”
Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods (2019, methodology): “But, if used indiscriminately, neural networks that have a large number of hidden layers and/or hidden neurons in each layer are prone to over-fitting data, leading to generalization errors [20,21].”
Rudiments of rough sets (2007, background): “te, that in statistics or machine learning such a data table is called a sample [56].”
Multivariate adaptive regression splines and neural network models for prediction of pile drivability (2016, methodology): “For the training data with N observations, GCV is calculated as (Hastie et al., 2009):”
pdp: An R Package for Constructing Partial Dependence Plots (2017, neutral): “Traditionally, for classiﬁcation problems, partial dependence functions are on a scale similar to the logit; see, for example, Hastie et al. (2009, pp. 369—370).”