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dc.contributor.authorSwingler, Kevinen_UK
dc.description.abstractA 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 with higher capacity than standard Hopfield networks. They can also be used for regression, clustering, classification, and as fitness models for use in heuristic optimisation. This paper presents a set of methods for estimating the values of the weights in a MOHN from training data. The different methods are compared to each other and to a standard MLP trained by back propagation and found to be faster to train than the MLP and more reliable as the error function does not contain local minima.en_UK
dc.publisherScience and Technology Publicationsen_UK
dc.relationSwingler K (2015) A Comparison of Learning Rules for Mixed Order Hyper Networks In: Proceedings of the 7th International Joint Conference on Computational Intelligence. NCTA (IJCCI), Setubal, Portugal. Setubal, Portugal: Science and Technology Publications, pp. 17-27.;
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectHigh Order Networksen_UK
dc.subjectLearning Rulesen_UK
dc.titleA Comparison of Learning Rules for Mixed Order Hyper Networksen_UK
dc.typeConference Paperen_UK
dc.rights.embargoreason[NCTA_MOHN_Learn_Final.pdf] : The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.type.statusVoR - Version of Recorden_UK
dc.citation.btitleProceedings of the 7th International Joint Conference on Computational Intelligenceen_UK
dc.citation.conferencelocationSetubal, Portugalen_UK
dc.citation.conferencenameNCTA (IJCCI)en_UK
dc.publisher.addressSetubal, Portugalen_UK
dc.contributor.affiliationComputing Scienceen_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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