RĂ¼diger Reischuk, Thomas Zeugmann

A new algorithm for learning one-variable pattern languages from positive data

is proposed and analyzed with respect to its average-case behavior.

We consider the total learning time that takes into account all

operations till convergence to a correct hypothesis is achieved.

For almost all meaningful distributions

defining how ...
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John Case, Sanjay Jain, RĂ¼diger Reischuk, Frank Stephan, Thomas Zeugmann

Presented is an algorithm (for learning a subclass of erasing regular

pattern languages) which

can be made to run with arbitrarily high probability of

success on extended regular languages generated by patterns

$\pi$ of the form $x_0 \alpha_1 x_1 ... \alpha_m x_m$

for unknown $m$ but known $c$,

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