On the Quasi-Minimal Solution of the General Covering Problem

By Ryszard S. Michalski. 1969

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Citations in the corpus

Learning Certifiably Optimal Rule Lists (2017, background): “, early algorithms [9, 15, 29]) and associative classi cation [41].”
Scalable Bayesian Rule Lists (2016, methodology): “Rule learning algorithms have been developed possibly since the AQ algorithm in the 1960s (Michalski, 1969) and throughout the 1980’s and 90’s (Cendrowska, 1987; Quinlan, 1983; Clark & Niblett, 1989; Cohen, 1995; Liu, Hsu, & Ma, 1998).”
The CN2 Induction Algorithm (1989, methodology): “The aq rule-generation algorithm has been described elsewhere (e.g. [8, 12, 13]), and the aqr system is an instance of this general algorithm. aqr generates a decision rule for each class in turn.”
Computational intelligence methods for rule-based data understanding (2004, background): “For over 30 years Michalski has been working on the family of AQ covering algorithms [51], creating more than 20 major versions.”
Dynamic weighted majority: a new ensemble method for tracking concept drift (2003, methodology): “aq-pm [6] uses the aq algorithm [ 16 ] to learn new rules from those stored in memory and from new ones from the input stream.”
SIRUS: making random forests interpretable (2019, methodology): “Rule learning originates from the inﬂuential AQ system of Michalski (1969).”
An Introduction to Machine Learning (2017, background): “The discipline got a major boost by the collection of papers edited by Michalski et al. [61].”
Machine Learning, Neural and Statistical Classification (2009, methodology): “We are here concerned with heading (b), exemplified by Michalski and Chilausky’s (1980) landmark use of the AQ11 algorithm (Michalski & Larson, 1978) to generate automatically a rule-based classifier for crop farmers.”
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts (2007, methodology): “AQ-PM (Maloof and Michalski, 2000) uses the AQ algorithm (Michalski, 1969), a batch learning algorithm, to form rules by simply reapplying the algorithm when new examples arrive.”
A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Associative classification and rule learning methods (e.g., Ma et al., 1998; Li et al., 2001; Yin and Han, 2003; Chen et al., 2006; Cheng et al., 2007; McCormick et al., 2012; Rudin et al., 2013; Dong et al., 1999; Michalski, 1969; Clark and Niblett, 1989; Frank and Witten, 1998) mine for frequent r”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, background): “Many algorithms are capable of learning rules or trees directly from a set of training examples, e.g., CN2 [15], AQ [45], RIPPER [16], AntMiner+ [44], C4.5 [49] or CART [11].”
The CN2 induction algorithm (2004, methodology): “Two families of systems, based on the ID3 (Quinlan, 1983) and AQ (Michalski, 1969) algorithms, have been especially successful.”