Identifying Left-Right Deterministic Linear Languages
Jorge Calera-Rubio and Jose Oncina
Lecture Notes in Artificial Intelligence
Left deteministic linear languages are a subclass of the context free languages that includes all the regular languages. Recently was proposed an algorithm to identify in the limit with polynomial time and data such class of languages. It was also pointed that a symetric class, the right deterministic linear languages is also identifiable in the limit from polynomial time and data. In this paper we show that the class of the Left-Right Deterministic Languages formed by the union of both classes is also identifiable. The resulting class is the largest one for wich this type of results has been obtained so far.
In this paper we also introduce the notion of $n$-negative characteristic sample, that is a sample that forces an inference algorithm to output an hypothesis of size bigger than $n$ when strings from a nonidentifiable language are provided.