An interference matching technique for inducing abstractions
Communications of the ACM
Inductive inference of VL decision rules
ACM SIGART Bulletin
Automatic generation of object class descriptions using symbolic learning techniques
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
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Some recent work in area of learning structural descriptions from examples is reviewed in light of the need in many diverse disciplines for programs which can perform conceptual data analysis. Such programs describe complex data interms or logical, functional, and causal relationships which cannot bediscovered using traditional data analysis techniques. Various important aspects of the problem of learning structural are examined and criteria for evaluating current work is presented. Methods published by Buchanan, et. al. [1-3, 20], Hayes-Roth [6-91, and Vere [22-25], are analyzed according tothese criteria and compared to a method developed by the authors. Finally some goals are suggested for future research.