Machine learning in artificial intelligence |
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| Affiliation: | 1. Department of Electrical Engineering, FET, Gomal University, Dera Ismail Khan, Pakistan;2. Department of Electrical Engineering, University of Engineering & Technology, Kohat Campus, Pakistan;3. Department of Electrical Engineering, Islamia University, Bahawalpur, Pakistan;4. Key Laboratory of Power System and Automation, Shandong University, China;1. McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary AB, Canada;2. Biomedical Engineering Graduate Program and Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary AB, Canada;3. Department of Rheumatology and Clinical Immunology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany;4. Department of Medicine, FAU University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany;5. Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary AB, Canada;6. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary AB, Canada;1. Laboratoire de Mécanique des Solides, CNRS, UMR 7649, Institut Polytechnique de Paris, Ecole Polytechnique, 91120 Palaiseau, France;2. SNCF Innovation and Research, 1-3 avenue François Mitterrand, 93210 la plaine Saint Denis, France |
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| Abstract: | Among several forms of learning, learning concepts from examples is the most common and best understood. In this paper some approaches to learning concepts from examples are reviewed. In particular those approaches that are currently most important with respect to practical applications (learning decision trees and if-then rules), or likely to become very important in the near future (Inductive Logic Programming as a form of relational learning) are discussed. |
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