Abstract
The class of very simple grammars is known to be polynomial-time identifiable in the limit from positive data. This paper gives even more general discussion on the efficiency of identification of very simple grammars from positive data, which includes both positive and negative results. In particular, we present an alternative efficient inconsistent learning algorithm for very simple grammars.
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References
Pitt, L.: Inductive inference, DFAs, and computational complexity. In: Jantke, K.P. (ed.) AII 1989. LNCS, vol. 397, pp. 18–44. Springer, Heidelberg (1989)
Lange, S., Wiehagen, R.: Polynomial-time inference of arbitrary pattern languages. New Generation Computing 8, 361–370 (1991)
de la Higuera, C.: Characteristic sets for polynomial grammatical inference. Machine Learning 27, 125–138 (1997)
Takada, Y.: Grammatical inference for even linear languages based on control sets. Information Processing Letters 28, 193–199 (1988)
Clark, A., Eyraud, R.: Identification in the limit of substitutable context-free languages. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 283–296. Springer, Heidelberg (2005)
Yokomori, T.: Polynomial-time identification of very simple grammars from positive data. Theoretical Computer Science 298, 179–206 (2003)
Wakatsuki, M., Teraguchi, K., Tomita, E.: Polynomial time identification of strict deterministic restricted one-counter automata in some class from positive data. In: Paliouras, G., Sakakibara, Y. (eds.) ICGI 2004. LNCS (LNAI), vol. 3264, pp. 260–272. Springer, Heidelberg (2004)
Yoshinaka, R.: Polynomial-time identification of an extension of very simple grammars from positive data. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds.) ICGI 2006. LNCS (LNAI), vol. 4201, pp. 45–58. Springer, Heidelberg (2006)
Yokomori, T.: Erratum to Polynomial-time identification of very simple grammars from positive data [Theoret. Comput. Sci. 298 (2003) 179–206]. Theoretical Computer Science 377(1-3), 282–283 (2007)
Yokomori, T.: On polynomial-time learnability in the limit of strictly deterministic automata. Machine Learning 19, 153–179 (1995)
Angluin, D.: Inference of reversible languages. Journal of the Association for Computing Machinery 29(3), 741–765 (1982)
Angluin, D.: Finding patterns common to a set of strings. Journal of Computer and System Sciences 21(1), 46–62 (1980)
Wakatsuki, M., Tomita, E.: A fast algorithm for checking the inclusion for very simple deterministic pushdown automata. IEICE transactions on information and systems E76-D(10), 1224–1233 (1993)
Wiehagen, R., Zeugmann, T.: Ignoring data may be the only way to learn efficiently. Journal of Experimental and Theoretical Artificial Intelligence 6, 131–144 (1994)
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Yoshinaka, R. (2007). Learning Efficiency of Very Simple Grammars from Positive Data. In: Hutter, M., Servedio, R.A., Takimoto, E. (eds) Algorithmic Learning Theory. ALT 2007. Lecture Notes in Computer Science(), vol 4754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75225-7_20
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DOI: https://doi.org/10.1007/978-3-540-75225-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75224-0
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