The Elements of Statistical Learning, Second Edition, Trevor Hastie, Robert Tishirani, Jerome Friedman. (2009), Springer Series in Statistics, ISBN 0172-7397, 745 pp

By Budiman Minasny. 2009

345 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f1bc5c27295345e38c5d55f03f541f0a8368f62b

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In Environmental Engineering

Citations in the corpus

The Kolmogorov-Arnold representation theorem revisited (2020, background): “CART or MARS for instance, select a partition of the input space by making successive splits along different directions and then fit a piecewise constant function on the selected partition [9], Section 9.”
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, neutral): “Another option for selection of λ is k-fold cross-validation, where k is a small number such as 5 or 10 (e.g. Hastie, Tibshirani and Friedman [37], Section 7.10.1).”
Algorithm runtime prediction: Methods & evaluation (2012, background): “…point s , we continue to the left child node if x i , j (cid:2) s (for real-valued variable j ) or x i , j ∈ s (for categorical variable j ), and to the right child node otherwise The predictive mean for x i is the constant c m in the leaf that this process selects; there is no variance predictor”
Deep learning for plant identification using vein morphological patterns (2016, methodology): “Three diﬀerent Machine Learning algorithms were tested: Support Vector Machines (SVM), Penalized Discriminant Analysis (PDA) and Random Forests (RF) [23].”
Unsupervised learning for text-to-speech synthesis (2013, methodology): “Both methods are mentioned and their results compared graphically in Hastie et al. (2009, pp. 593–4). The second type of variable importance described forms the basis of the feature subset selection method presented in Tuv et al. (2009). The main innovation there is the introduction of significance ”
What You Can and Can’t Properly Do with Regression (2010, background): “Among the more popular approaches are multivariate statistics (Anderson 1958; Gifi 1990), exploratory data analysis (Tukey 1977; Diaconis 1985), and more recently, dynamic graphics (Cook and Swayne 2007) and Machine/Statistical Learning (Hastie et al. 2009; Berk 2008).”
Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting (2016, methodology): “For Series with the indicator basis, this yields a classification accuracy of 94.62% (±1.00%), which is competitive with state-of-the-art classifiers (see, e.g., Hastie, Tibshirani, and Friedman 2009).”
A comparison of deep networks with ReLU activation function and linear spline-type methods (2018, methodology): “MARS is a widely applied method in statistical learning [11].”
A random forest guided tour (2015, background): “Similarly, Friedman et al. (2009) decompose the variance of the forest as a product of the correlation between trees and the variance of a single tree.”
Consistency of Random Forests (2014, methodology): “Additive models also play an important role in the context of high-dimensional data analysis and sparse modelling, where they are successfully involved in procedures such as the Lasso and various aggregation schemes (for an overview, see, e.g., Hastie et al., 2009).”
Neural Random Forests (2016, neutral): “The monographs by Breiman et al. (1984), Devroye et al. (1996), Rokach and Maimon (2008), and Hastie et al. (2009) will provide the reader with introductions to the general subject area, both from a practical and theoretical perspective.”
Forecasts of Violence to Inform Sentencing Decisions (2013, methodology): “In effect, a Bayes classiﬁer is employed (Hastie et al. 2009: 21).”