| LEARNING FLEXIBLE CONCEPTS USING A TWO-TIERED REPRESENTATION (2007) | |||||||||||||||
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| 'Most human concepts are flexible in the sense that they inherently lack: precise boundaries, and these boundaries are often contextdependent. 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:haracterizes allowable concept modifications and contextdependency. The proposed method has been implemented in the POSEIDON 3 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. 1 On leave of absence from the University of Torino, Italy 2 On leave of absence from the Univerity of Ottawa, Canada. 3 The system is named after POSEIDON, the Greek god of the sea, water and waves, which represent fluidity and changing aspects of natuxe. | |||||||||||||||
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