Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
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The correct definition of finite elasticity: corrigendum to identification of unions
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
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COLT '96 Proceedings of the ninth annual conference on Computational learning theory
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Theoretical Computer Science
Inductive inference of unbounded unions of pattern languages from positive data
Theoretical Computer Science - Special issue on algorithmic learning theory
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Theoretical Computer Science - Algorithmic learning theory
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Information and Computation
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Topological Properties of Concept Spaces
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
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Information and Computation
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JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
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This paper gives a proof that the class of unbounded unions of languages of regular patterns with constant segment length bound is inferable from positive data with mind change bound between ωω and . We give a very tight bound on the mind change complexity based on the length of the constant segments and the size of the alphabet of the pattern languages. This is, to the authors' knowledge, the first time a natural class of languages has been shown to be inferable with mind change complexity above ωω. The proof uses the notion of closure operators on a class of languages, and also uses the order type of well-partial-orderings to obtain a mind change bound. The inference algorithm presented can be easily applied to a wide range of classes of languages. Finally, we show an interesting connection between proof theory and mind change complexity.