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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--Wiehagen [7] algorithm with respect to its averagecase time behavior until convergence. This algorithm learns the class of... (Update)
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BibTeX entry: (Update)
@misc{ rossmanith-kvariable,
author = "Peter Rossmanith and Thomas Zeugmann",
title = "k-Variable Pattern Languages Efficiently Stochastically Finite on Average
from Positive Data",
url = "citeseer.ist.psu.edu/77661.html" }
Citations (may not include all citations):412 Language identification in the limit (context) - Gold - 1967
156 Inductive inference of formal languages from positive data (context) - Angluin - 1980
142 Finding patterns common to a set of strings (context) - Angluin - 1980
74 Formal Principles of Language Acquisition (context) - Wexler, Culicover - 1980
61 Polynomial-time inference of arbitrary pattern languages (context) - Lange, Wiehagen - 1991
36 A polynomial-time algorithm for learning k--variable pattern.. (context) - Kearns, Pitt - 1991
34 Pattern inference (context) - Shinohara, Arikawa - 1995
34 The Formal Language Theory Column - Salomaa, Return - 1994
31 the complexity of inductive inference (context) - Daley, Smith - 1986
24 Pattern languages are not learnable (context) - Schapire - 1990
22 DFAs and computational complexity (context) - Pitt - 1989
17 Learning string patterns and tree patterns from examples (context) - Ko, Marron et al. - 1990
8 Lange and Wiehagen's pattern learning algorithm: An averagec.. (context) - Zeugmann - 1998
8 variable pattern languages efficiently stochastically finite.. (context) - Rossmanith, Zeugmann - 1998
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