This paper revisits the theory of regular inference, in particular by extending the definition of
structural completeness of a positive sample and by demonstrating two basic theorems. This …

Frequent itemset mining (FIM) faces significant challenges with the expansion of large-scale
datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor …

We sketch possible applications of grammatical inference techniques to problems arising in
the context of XML. The idea is to infer document type definitions (DTDs) of XML documents …

We show how appropriately chosen functions f which we call distinguishing can be used to
make deterministic finite automata backward deterministic. This idea can be exploited to …

We study the problem of learning regular tree languages from text. We show that the
framework of function distinguishability as introduced in our ALT 2000 paper is …

In this paper, a new formalism that permits to represent a non-trivial class of context-
sensitive languages, the Augmented Regular Expressions (AREs), is introduced. AREs …

LNAI 1891 - Permutations and Control Sets for Learning Non-regular Language Families Page
1 Permutations and Control Sets for Learning Non-regular Language Families Henning Fernau1 …

L'objet de cette étude est l'apprentissage automatique d'une grammaire formelle à partir
d'exemples. En particulier, nous nous concentrons sur l'inférence de grammaires régulières …

We show how appropriately chosen functions which we call distinguishing can be used to
make deterministic finite automata backward deterministic. These ideas can be exploited to …

This paper describes a form of Markov chain, called a constrained Markov network, and its
inference from finite-length sequences over a finite alphabet as a structural/statistical model …