Learning Hard Concepts Through Constructive Induction
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Based on research that pinpoints the basic deficiency of current                empirical learning algorithms, the proposed project will develop a              principled system to complement them.  The combination of                       traditional with new algorithms is designed to improve overall                  system speed, accuracy, and generality.  To develop a general                   learning system that will perform well over many kinds of concepts,             domains, and situations, this project will: design a uniform                    approach that reduces "hard" concept learning to the problem of                 learning "easy" concepts that current systems can manage; allow                 flexible learning despite varied learning situations, including                 uncertainty, incremental demands, and differing amounts and kinds of            knowledge; demonstrate in several ways the utility of the approach,             including progress on significant problems such as protein folding.             In a new architecture for concept learning, the project will: speed             reliable learning by using a uniform approach (essentially a single             algorithm) in several stages of progressive, "layered" construction             from lower level components, using and acquiring different kinds of             knowledge in appropriate information structures.
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{'Code': '6835', 'Text': 'KNOWLEDGE & DATABASE SYSTEMS'}
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