By the same authors

Improving the associative rule chaining architecture

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

Standard

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-Verlag, 2013. p. 98-105 (Lecture Notes in Computer Science; Vol. 8131).

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

Harvard

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-Verlag, Berlin, pp. 98-105. https://doi.org/10.1007/978-3-642-40728-4_13

APA

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-Verlag. https://doi.org/10.1007/978-3-642-40728-4_13

Vancouver

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-Verlag. 2013. p. 98-105. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-40728-4_13

Author

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-Verlag, 2013. pp. 98-105 (Lecture Notes in Computer Science).

Bibtex - Download

@inbook{c588ba40c7a7410bae895728b02ec162,
title = "Improving the associative rule chaining architecture",
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.",
author = "Burles, {Nathan John} and Simon O'Keefe and Jim Austin",
year = "2013",
doi = "10.1007/978-3-642-40728-4_13",
language = "English",
isbn = "978-3-642-40727-7",
volume = "8131 LNCS",
series = "Lecture Notes in Computer Science",
publisher = "Springer-Verlag",
pages = "98--105",
editor = "V Mladenov and Palm, {G } and B Appollini and P Koprinkova-Hristova and A Villa and N Kasabov",
booktitle = "Artificial Neural Networks and Machine Learning - ICANN 2013",
address = "Germany",

}

RIS (suitable for import to EndNote) - Download

TY - CHAP

T1 - Improving the associative rule chaining architecture

AU - Burles, Nathan John

AU - O'Keefe, Simon

AU - Austin, Jim

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-40728-4_13

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

M3 - Chapter (peer-reviewed)

SN - 978-3-642-40727-7

VL - 8131 LNCS

T3 - Lecture Notes in Computer Science

SP - 98

EP - 105

BT - Artificial Neural Networks and Machine Learning - ICANN 2013

A2 - Mladenov, V

A2 - Palm, G

A2 - Appollini, B

A2 - Koprinkova-Hristova, P

A2 - Villa, A

A2 - Kasabov, N

PB - Springer-Verlag

CY - Berlin

ER -