Abstract
Algorithms that infer deterministic finite automata from given data and that comply with the identification in the limit condition have been thoroughly tested and are in practice often preferred to elaborate heuristics. Even if there is no guarantee of identification from the available data, the existence of associated characteristic sets means that these algorithms converge towards the correct solution. In this paper we construct a framework for algorithms with this property, and consider algorithms that use the quantity of information to direct their strategy. These data dependent algorithms still identify in the limit but may require an exponential characteristic set to do so. Nevertheless preliminary practical evidence suggests that they could perform better.
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de la Higuera, C., Oncina, J., Vidal, E. (1996). Identification of DFA: Data-dependent versus data-independent algorithms. In: Miclet, L., de la Higuera, C. (eds) Grammatical Interference: Learning Syntax from Sentences. ICGI 1996. Lecture Notes in Computer Science, vol 1147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033365
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DOI: https://doi.org/10.1007/BFb0033365
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