Applications of machine learning and rule induction

By Pat Langley, Herbert A. Simon. 1995

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Rule learning approaches have been extensively used for knowledge representation in expert systems [192] .”
Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia (2019, background): “Medicine, finance and law, are a few domains where humans rely on an algorithms to solve expert tasks [28].”
Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, background): “4 In each iteration, managers assess the current model ’ s quality, subsequently deciding on how to proceed (Langley & Simon, 1995).”
Applying data mining to telecom churn management (2006, background): “…data mining technology can provide business intelligence to generate new opportunities (Lau, Wong, Hui, & Pun, 2003; Su, Hsu, & Tsai, 2002; Zhang, Hu, Patuwo, & Indro, 1999; Langley & Simon, 1995; Bortiz & Kennedy, 1995; Fletcher & Goss, 1993; Salchenberger, Cinar, & Lash, 1992; Tam & Kiang, 1992).”
Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, unknown): “10 Polynomials [54], and inductive learning [55].”
Evolving rule-based systems in two medical domains using genetic programming (2004, unknown): “Inductive learning tools and techniques have been widely applied during the last two decades in various domains [30] for their comprehensibility, as well as for their ability to generalize from processing with large databases and high complexity domains of application.”
Metamodels for Computer-based Engineering Design: Survey and recommendations (2001, background): “” Additional examples can be found elsewhere [35,34].”
Machine Learning and Software Engineering (2002, unknown): “As observed in ( Langley and Simon, 1995 ), the power of machine learning methods does not come from a particular induction method, but instead from proper formulation of the problems and from crafting the representation to make learning tractable.”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, unknown): “Other types of models include radial basis functions (RBF) [74,75], multivariate adaptive regression splines (MARS) [76], least interpolating polynomials [77] and inductive learning [78].”
Beyond predictive accuracy : what? (1998, unknown): “The number of elded applications has grown steadily, evidence that industry needs and uses ML technology [13].”
Extracting decision trees from trained neural networks (1999, methodology): “In this paper we present a methodology for extracting decision trees from input data generated from trained neural networks instead of doing it directly from the data.”
Pattern recognition and neural networks (1996, background): “Langley & Simon (1995) and Bratko & Muggleton (1995) discuss applications of machine learning with claimed real-world benefits.”
Application of data mining techniques in customer relationship management: A literature review and classification (2009, unknown): “…data, data mining technology can provide business intelligence to generate new opportunities (Bortiz & Kennedy , 1995; Fletcher & Goss, 1993; Langley & Simon, 1995; Lau, Wong, Hui, & Pun, 2003; Salchenberger, Cinar, & Lash, 1992; Su, Hsu, & Tsai, 2002; Tam & Kiang, 1992; Zhang, Hu, Patuwo,…”
Underwater Internet of Things in Smart Ocean: System Architecture and Open Issues (2020, methodology): “Machine learning is described as the adoption of computational methods for improving the machine performance by detecting and describing consistencies and patterns in training data [37].”