Abstract
We have presented in this paper a learning system that is able to produce characteristic and discriminant versions of disjunctive target concepts. The algorithm is able to process all classes at once. It integrates several inductive learning methods: An ID3-based system (with several important improvements over the original ID3) and a generalization engine. The first way we approached integration was to use a switchbox mecanism that enabled us to quickly identify strong and weak points of each learning systems. The switchbox integration was also used to test plausible ways of fully merging the systems. When the limit of what could possibly be done was reached, we then went on integrating totally the learning systems. This required major changes in the systems themselves and was achieved by designing a powerful object-based representation formalism and implementing a common pattern-matcher. Following this work, several problems remained to be solved and lead us to experiment with other learning systems such as the star algorithm. The integrated system can use domain knowledge and is being applied on large scale problems.
Fundings for this research has been provided by the European Economic Community under ESPRIT contract P1063, the INSTIL project. The partners of INSTIL are GEC research (UK), LRI (F), and Cognitech (F). LRI's research has also been supported by the GRECO and PRC "Intelligence Artificielle" and by Intellisoft.
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© 1989 Springer-Verlag Berlin Heidelberg
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Manago, M., Blythe, J. (1989). Learning disjunctive concepts. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017224
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DOI: https://doi.org/10.1007/BFb0017224
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