Programs for Machine Learning

By Steven L. Salzberg, Alberto Maria Segre. 1994

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In Statistical Learning Theory

Citations in the corpus

Computing Optimal Decision Sets with SAT (2020, background): “) The objective of classification in machine learning is to devise a function φ̂ that matches the actual function φ on the training data E and generalizes suitably well on unseen test data [19,25,40,47].”
MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020, methodology): “Examples of these algorithms include CART, originally proposed by Breiman et al. (1984), and C4.5 by Quinlan (1993).”
Optimal Sparse Decision Trees (2019, methodology): “5 [19] decision tree algorithms (and other greedy tree-growing variants) have remained as dominant methods in practice.”
Learning customized and optimized lists of rules with mathematical programming (2018, background): “5 [53] would consider the full dataset, and select a feature to “split” on to form the top of the tree.”
Boolean Decision Rules via Column Generation (2018, background): “Rule sets stand in contrast to decision lists [44, 35, 49, 3, 34, 53], where rules are ordered in an IF-ELSE sequence, and decision trees [11, 43, 6], where they are organized into a tree structure.”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, neutral): “A popular approach to interpretable models is rule-based models, such as decision trees [6, 31] and decision lists [34], because they strike a balance between the two objectives.”
The Random Subspace Method for Constructing Decision Forests (1998, methodology): “5 algorithm [26], supervised or unsupervised clustering, distribution map matching [13], and support vector machines [33] can be readily applied.”
CPAR: Classification based on Predictive Association Rules (2003, background): “5 [8], RIPPER [3], CBA [7] and CMAR [6].”
Syntax-guided synthesis (2013, background): “0 [37,36], and is extremely scalable and has an inductive bias to learn smaller trees.”