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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Information and Computation
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COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Ordinal mind change complexity of language identification
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
Generalized notions of mind change complexity
Information and Computation
Topological properties of concept spaces (full version)
Information and Computation
Inferability of closed set systems from positive data
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Mind change efficient learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Set systems: Order types, continuous nondeterministic deformations, and quasi-orders
Theoretical Computer Science
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This paper shows that the mind change complexity of inferring from positive data the class of unbounded unions of languages of regular patterns with constant segment length bound is of the form @w^@w^^^@a+@b, assuming that the patterns are defined over a finite alphabet containing at least two elements. Here @a and @b are natural numbers, and we give tight bounds on their values 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 @w^@w. 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.