Abstract
Although experts have difficulty formulating their knowledge explicitly as
rules, they find it easy to demonstrate their expertise in specific
situations. Schemes for learning concepts from examples offer the
potential for domain experts to interact directly with machines to
transfer knowledge. Concept learning methods divide into similarity-based,
hierachical, function induction, and explanation-based
knowledge-intensive techniques. These are described, classified according
to input and output representations, and related to knowledge acquisition
for expert systems. Systems discussed include candidate elimination,
version space, ID3, PRISM, MARVIN, NODDY, BACON, COPER, and LEX-II.
Teaching requirements are also analyzed.
Notes
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