Abstract
Within the field of structural pattern analysis, algorithms for inference of discrete mathematical models from samples are an important area of research. This paper gives an extensive survey of state-of-the-art methods for data-driven inductive inference of finite-state automata. In addition to providing notationally consistent descriptions of the methods’ fundamental mode of operation, aspects such as sequential learning, advantages and disadvantages, and the extension to stochastic automata are also addressed.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Paper version not known
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Pattern Recognition and Artificial Intelligence
Paper Title
Journal
Date
