Data-driven inductive inference of finite-state automata
J Gregor - International Journal of Pattern Recognition and …, 1994 - World Scientific
International Journal of Pattern Recognition and Artificial Intelligence, 1994•World Scientific
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.
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.
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.
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