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.
Preview
Unable to display preview. Download preview PDF.
Bibliography
Angluin D. (1987). Queries and concept learning. Machine Learning 2, 319–342.
Dupont P., Miclet L. & Vidal E. (1994). What is the search space of the regular inference? Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 25–37). Lecture Notes in Artificial Intelligence 862, Springer-Verlag. Edited by R. Carrasco and J. Oncina.
Dupont P. (1994). Regular Grammatical Inference from positive and negative samples by genetic search: the GIG method. Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 236–245).Springer-Verlag Series in Artificial Intelligence 862. Edited by R. Carasco and J. Oncina.
Gold E.M. (1967). Language identification in the limit. Inform.&Control. 10, 447–474.
Gold E.M. (1978). Complexity of Automaton Identification from given Data. Information and Control 37, 302–320.
Harrison M.A. (1978). Introduction to Formal Language Theory. Reading: Addison-Wesley.
de la Higuera C. (1995). Characteristic sets for Grammatical Inference. In Proceedings of the International Colloquium on Grammatical Inference ICGI-96.
Koshiba, T., Mäkinen, E. & Takada, Y. (1995). Learning Deterministic Even Linear Languages from Positive Examples. Proceedings of ALT'95, Lecture Notes in Artificial Intelligence 997, Springer-Verlag.
Lang K.J. (1992). Random DFA's can be approximately Learned from Sparse Uniform Examples, Proceedings of COLT 1992, pp 45–52.
Miclet L. & de Gentile C. Inférence Grammaticale à partir d'Exemples et de Contre-exemples: deux algorithmes optimaux (BIG et RIG) et une version Heuristique (BRIG), Actes des JFA-94, Strasbourg, France, pp. F1–F13,1994.
Oncina J. & García P. (1992) Inferring Regular Languages in Polynomial Updated Time. In Pattern Recognition and Image Analysis, World Scientific (49–61).
Oncina J., García P. & Vidal E. (1993). Learning subsequential transducers for pattern recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 448–458.
Pitt, L. (1989). Inductive inference, dfas and computational complexity. Proceedings of the International Workshop on Analogical and Inductive Inference (pp. 18–44). Lecture Notes in Artificial Intelligence 397, Springer-Verlag.
Sempere J.M. & García P. (1994). A characterisation of Even Linear Languages and its application to the Learning Problem. Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 38–44). Lecture Notes in Artificial Intelligence 862, Springer-Verlag.
Sakakibara Y. (1992). Efficient Learning of Context-free Grammars from Positive Structural Examples. Inf. and Comp. 97, 23–60.
Takada Y. (1988). Grammatical inference for even Linear languages based on control sets. Information Processing Letters 28, 193–199.
Takada Y. (1994). A Hierarchy of Language Families Learnable by Regular Language Learners. Proceedings of the International Colloquium on Grammatical Inference ICGI-94 (pp. 16–24). Lecture Notes in Artificial Intelligence 862, Springer-Verlag.
Tomita M. (1982). Dynamic construction of Finite Automata from Examples Using Hill Climbing, Proc. of the 4th annual Cognitive Science Conference, USA, pp. 105–108.
Trakhenbrot B. & Barzdin Y.(1973). Finite automata: Behavior and Synthesis. North Holland Pub., Amsterdam.
Yokomori T. (1993). Learning non deterministic Finite Automata from queries and Counterexamples. Machine Intelligence 13. Furukawa, Michie & Muggleton eds., Oxford Univ. Press.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/BFb0033365
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61778-5
Online ISBN: 978-3-540-70678-6
eBook Packages: Springer Book Archive