A Nearest Hyperrectangle Learning Method

By Steven L. Salzberg. 1991

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Citations in the corpus

Interpretable Two-level Boolean Rule Learning for Classification (2015, background): “, 2012): covering (Rivest, 1987; Clark & Niblett, 1989; Cohen, 1995; Fürnkranz, 1999), bottom-up ( Salzberg, 1991; Domingos, 1996), multi-phase ( Liu et al.”
Boolean Decision Rules via Column Generation (2018, background): “see also the survey [30]) of learning rules one by one and removing “covered” examples, a bottomup strategy of combining more specific rules into more general ones [45, 22, 41], and associative classification in which association rule mining is followed by rule selection using various criteria [38, ”
Learning in the presence of concept drift and hidden contexts (2004, background): “Also, instance-based algorithms are known tobe sensitive to attribute relevance: irrelevant attributes have a detrimental e ecton predictive accuracy, though some approaches to improve on this have recentlybeen suggested (Salzberg, 1991; Cost & Salzberg, 1993).”
Neighborhood classifiers (2008, background): “Salzberg (1991) proposed a family of learning algorithms based on nested generalized exemplars (NGE), where an exemplar is a single training example, and generalized exemplars is an axis-parallel hyperrectangle that may cover several training examples.”
Advances in Instance Selection for Instance-Based Learning Algorithms (2002, methodology): “In previous work ( Brighton, 1997 ) we have shown that some of the concepts introduced by Smyth and Keane transfer to the simpler context of the nearest neighbour classification algorithm.”
Unifying Instance-Based and Rule-Based Induction (1996, methodology): “We have also found that use of the SVDM substantially attenuates this problem for symbolic attributes, as long as a large number of examples is available.”
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms (1997, methodology): “For example, Wettschereck and Dietterich (1995) showed that a mutual information method traded oo higher computational complexity for higher accuracies when compared with an online algorithm in the context of learning hyperrectangles (Salzberg, 1991).”
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features (2004, methodology): “Based on these results , and our ear - lier results on real - valued domains ( Salzberg , 1990 , 1991 ) , we conclude that exemplar weights ooer real potential for enhancing the power of practical learning algorithms .”
Learning in the Presence of Concept Drift and Hidden Contexts (1996, background): “Also, instance-based algorithms are known to be sensitive to attribute relevance: irrelevant attributes have a detrimental eeect on the predictive accuracy (though some approaches to improve on this have recently been suggested|e.g., Salzberg, 1991; Cost and Salzberg, 1993).”
Reduction Techniques for Instance-Based Learning Algorithms (2000, methodology): “Other exemplar-based machine learning paradigms include memory-based reasoning (Stanfill & Waltz, 1986), exemplar-based generalization (Salzberg, 1991; Wettschereck & Dietterich, 1995), and case-based reasoning (CBR) (Watson & Marir, 1994).”
Very Simple Classification Rules Perform Well on Most Commonly Used Datasets (1993, unknown): “3, EACH with feature adjustment (Salzberg, 1991) 95.”
Rule Extraction from Trained Support Vector Machines (2005, background): “Four different trials were run, and the final results were an average of those trials [ 7 ].”