McQueen, T, Hopgood, AA, Allen, TJ ORCID: https://orcid.org/0000-0001-6228-0237 and Tepper, JA ORCID: https://orcid.org/0000-0001-7339-0132, 2005. Extracting finite structure from infinite language. Knowledge-Based Systems, 18 (4-5), pp. 135-141. ISSN 0950-7051
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Abstract
This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set.
Item Type: | Journal article |
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Publication Title: | Knowledge-Based Systems |
Creators: | McQueen, T., Hopgood, A.A., Allen, T.J. and Tepper, J.A. |
Publisher: | Elsevier (not including Cell Press) |
Place of Publication: | Amsterdam |
Date: | 31 May 2005 |
Volume: | 18 |
Number: | 4-5 |
ISSN: | 0950-7051 |
Identifiers: | Number Type 10.1016/j.knosys.2004.10.010 DOI |
Divisions: | Schools > School of Science and Technology |
Record created by: | EPrints Services |
Date Added: | 09 Oct 2015 09:51 |
Last Modified: | 04 Feb 2022 12:33 |
URI: | https://irep.ntu.ac.uk/id/eprint/3813 |
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