Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26263
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Swingler, Kevin
Contact Email: kms@cs.stir.ac.uk
Title: High capacity content addressable memory with mixed order hyper networks
Editors: Merelo, JJ
Rosa, A
Cadenas, JM
Correia, AD
Mandani, K
Ruano, A
Filipe, J
Citation: Swingler K (2017) High capacity content addressable memory with mixed order hyper networks In: Merelo JJ, Rosa A, Cadenas JM, Correia AD, Mandani K, Ruano A, Filipe J (ed.) Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers, Cham, Switzerland: Springer. Computational Intelligence International Joint Conference, IJCCI 2015, 12.11.2015 - 14.11.2015, Lisbon, Portugal, pp. 337-358.
Issue Date: 2017
Series/Report no.: Studies in Computational Intelligence, 669
Conference Name: Computational Intelligence International Joint Conference, IJCCI 2015
Conference Dates: 2015-11-12T00:00:00Z
Conference Location: Lisbon, Portugal
Abstract: A mixed order hyper network (MOHN) is a neural network in which weights can connect any number of neurons, rather than the usual two. MOHNs can be used as content addressable memories (CAMs) with higher capacity than standard Hopfield networks. They can also be used for regression learning of functions in ƒ : {−1,1}n→R in which the turning points are equivalent to memories in a CAM. This paper presents a number of methods for learning an energy function from data that can act as either a CAM or a regression model and presents the advantages of using such an approach.
Status: Book Chapter: publisher version
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