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ICGI
1998
Springer

Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data

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Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate this approach on the concept class of all pattern languages. The transformation of learning in the limit into stochastically finite learning with high confidence is achieved by first analyzing the Lange
Peter Rossmanith, Thomas Zeugmann
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where ICGI
Authors Peter Rossmanith, Thomas Zeugmann
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