Learning approximately regular languages with reversible languages

Approximately Learning Regular Languages with respect to Reversible Languages: A Rough Set Based Analysis

an assumption which most of the learning frameworks take that a target concept to be identi ed should exist in a xed class of concepts, called a hypothesis space. This assumption often causes the divergence of the learning process in case that the target concept could be outside the hypothesis space. Thus, it is one of the most challenging research in machine learning community to study on a fr...

Learning Regular Omega Languages

We provide an algorithm for learning an unknown regular set of infinite words, using membership and equivalence queries. Three variations of the algorithm learn three different canonical representations of omega regular languages, using the notion of families of dfas. One is of size similar to L$, a dfa representation recently learned using L ∗ [7]. The second is based on the syntactic forc, in...

Learning Commutative Regular Languages

TOPOLOGICAL CHARACTERIZATION FOR FUZZY REGULAR LANGUAGES

We present a topological characterization for fuzzy regular languages: we show that there is a bijective correspondence between fuzzy regular languages and the set of all clopen fuzzy subsets with finite image in the induced fuzzy topological space of Stone space (Profinite space), and then we give a representation of closed fuzzy subsets in the induced fuzzy topological space via fuzzy regular...

Learning Reversible Languages with Terminal Distinguishability

k-reversible languages are regular ones that offer interesting properties under the point of view of identification of formal languages in the limit. Different methods have been proposed to identify k-reversible languages in the limit from positive samples. Non-regular language classes have been reduced to regular reversible languages in order to solve their associated learning problems. In thi...