Improving the associative rule chaining architecture

Nathan John Burles; Simon O'Keefe; Jim Austin

doi:10.1007/978-3-642-40728-4_13

Improving the associative rule chaining architecture

Nathan John Burles, Simon O'Keefe, Jim Austin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

Abstract

This paper describes improvements to the rule chaining architecture presented in [1]. The architecture uses distributed associative memories to allow the system to utilise memory eciently, and superimposed distributed representations in order to reduce the time complexity of a tree search to O(d), where d is the depth of the tree. This new work reduces the memory required by the architecture, and can also further reduce the time complexity.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning - ICANN 2013
Subtitle of host publication	23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.
Editors	V Mladenov, G Palm, B Appollini, P Koprinkova-Hristova, A Villa, N Kasabov
Place of Publication	Berlin
Publisher	Springer
Pages	98-105
Number of pages	8
Volume	8131 LNCS
ISBN (Electronic)	978-3-642-40728-4
ISBN (Print)	978-3-642-40727-7
DOIs	https://doi.org/10.1007/978-3-642-40728-4_13
Publication status	Published - 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	8131
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Access to Document

10.1007/978-3-642-40728-4_13

Improving the Associative Rule Chaining Architecture
© 2013, Springer-Verlag Berlin Heidelberg. This is an author produced version of a paper published in Lecture Notes in Computer Science, 8131. Uploaded in accordance with the publisher's self-archiving policy
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Cite this

Burles, N. J., O'Keefe, S., & Austin, J. (2013). Improving the associative rule chaining architecture. In V. Mladenov, G. Palm, B. Appollini, P. Koprinkova-Hristova, A. Villa, & N. Kasabov (Eds.), Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings. (Vol. 8131 LNCS, pp. 98-105). (Lecture Notes in Computer Science; Vol. 8131). Springer. https://doi.org/10.1007/978-3-642-40728-4_13

Burles, Nathan John ; O'Keefe, Simon ; Austin, Jim. / Improving the associative rule chaining architecture. Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.. editor / V Mladenov ; G Palm ; B Appollini ; P Koprinkova-Hristova ; A Villa ; N Kasabov. Vol. 8131 LNCS Berlin : Springer, 2013. pp. 98-105 (Lecture Notes in Computer Science).

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Burles, NJ , O'Keefe, S & Austin, J 2013, Improving the associative rule chaining architecture. in V Mladenov, G Palm, B Appollini, P Koprinkova-Hristova, A Villa & N Kasabov (eds), Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.. vol. 8131 LNCS, Lecture Notes in Computer Science, vol. 8131, Springer, Berlin, pp. 98-105. https://doi.org/10.1007/978-3-642-40728-4_13

Improving the associative rule chaining architecture. / Burles, Nathan John ; O'Keefe, Simon ; Austin, Jim.
Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.. ed. / V Mladenov; G Palm; B Appollini; P Koprinkova-Hristova; A Villa; N Kasabov. Vol. 8131 LNCS Berlin: Springer, 2013. p. 98-105 (Lecture Notes in Computer Science; Vol. 8131).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)

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Burles NJ , O'Keefe S , Austin J. Improving the associative rule chaining architecture. In Mladenov V, Palm G, Appollini B, Koprinkova-Hristova P, Villa A, Kasabov N, editors, Artificial Neural Networks and Machine Learning - ICANN 2013: 23rd International Conference on Artificial Neural Networks, Sofia, Bulgaria, September 2013. Proceedings.. Vol. 8131 LNCS. Berlin: Springer. 2013. p. 98-105. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-40728-4_13