Approximative Learning of Regular Languages

Fernau, Henning

doi:10.1007/3-540-45627-9_19

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Cited by 5 publications

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“…Another interesting property of each class f -DL of function distinguishable languages is established in [6]: the approximability of the whole class of regular languages in the sense that, given any regular language L, a learning algorithm for f -DL infers, given L, the smallest language L ∈ f -DL including L.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for Learning Function Distinguishable Regular Languages

Fernau

Radl

2002

Lecture Notes in Computer Science

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Section: Introductionmentioning

confidence: 99%

Algorithms for Learning Function Distinguishable Regular Languages

Fernau

Radl

2002

Lecture Notes in Computer Science

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Learning XML Grammars

Fernau

2001

Machine Learning and Data Mining in Pattern Recognition

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Learning Tree Languages from Text

Fernau

2002

Lecture Notes in Computer Science

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We study the problem of learning regular tree languages from text. We show that the framework of function distinguishability, as introduced by the author in [Theoret. Comput. Sci. 290 (2003) 1679-1711], can be generalized from the case of string languages towards tree languages. This provides a large source of identifiable classes of regular tree languages. Each of these classes can be characterized in various ways. Moreover, we present a generic inference algorithm with polynomial update time and prove its correctness. In this way, we generalize previous works of Angluin, Sakakibara and ourselves. Moreover, we show that this way all regular tree languages can be approximately identified.

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Approximative Learning of Regular Languages

Cited by 5 publications

References 9 publications

Algorithms for Learning Function Distinguishable Regular Languages

Algorithms for Learning Function Distinguishable Regular Languages

Learning XML Grammars

Learning Tree Languages from Text

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