Abstract
Most human concepts are flexible in the sense that they inherently lack precise boundaries, and these boundaries are often context-dependent. This chapter describes a method for representing and inductively learning flexible concepts from examples. The basic idea is to represent such concepts using a two-tiered representation. Such a representation consists of two structures (“tiers”): the Base Concept Representation (BCR), which captures explicitly the basic and context-independent concept properties, and Inferential Concept Interpretation (ICI), which characterizes allowable concept modifications and context-dependency. The proposed method has been implemented in the POSEIDON system (also called AQ16), and tested on various practical problems, such as learning the concept of “Acceptable union contracts” and “Voting patterns of Republicans and Democrats in the U.S. Congress.” In the experiments, the system generated concept descriptions that were both, more accurate and simpler than those produced by other methods tested, such as methods employing simple exemplar-based representations, decision tree learning, and some previous methods for rule learning.
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Michalski, R.S., Bergadano, F., Matwin, S., Zhang, J. (1993). Learning Flexible Concepts Using a Two-Tiered Representation. In: Meyrowitz, A.L., Chipman, S. (eds) Foundations of Knowledge Acquisition. The Springer International Series in Engineering and Computer Science, vol 195. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-27366-2_5
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DOI: https://doi.org/10.1007/978-0-585-27366-2_5
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