Iterative learning of simple external contextual languages

Iterative Learning of Simple External Contextual Languages

It is investigated for which choice of a parameter q, denoting the number of contexts, the class of simple external contextual languages is iteratively learnable. On the one hand, the class admits, for all values of q, polynomial time learnability provided an adequate choice of the hypothesis space is given. On the other hand, additional constraints like consistency and conservativeness or the ...

On external contextual grammars with subregular selection languages

In this paper, we study the power of external contextual grammars with selection languages from subfamilies of the family of regular languages. If we consider families Fn which are obtained by restriction to n states or nonterminals or productions or symbols to accept or to generate regular languages, we obtain four infinite hierarchies of the corresponding families of languages generated by ex...

Neural learning of approximate simple regular languages

Discrete-time recurrent neural networks (DTRNN) have been used to infer DFA from sets of examples and counterexamples; however, discrete algorithmic methods are much better at this task and clearly outperform DTRNN in space and time complexity. We show, however, how DTRNN may be used to learn not the exact language that explains the whole learning set but an approximate and much simpler languag...

Learning Regular Languages From Simple Positive Examples

Learning (k, l)-Contextual Tree Languages for Information Extraction

Learning regular languages from positive examples only is known to be infeasible. A common solution is to define a learnable subclass of the regular languages. In the past, this has been done for regular string languages. Using ideas from those techniques, we define a learnable subclass of regular unranked tree languages, called the (k,l)-contextual tree languages. We describe the use of this s...