STORRE
STORRE: Stirling Online Research Repository
http://hdl.handle.net/1893/26263| Appears in Collections: | Computing Science and Mathematics Conference Papers and Proceedings |
| Peer Review Status: | Refereed |
| Author(s): | Swingler, Kevin |
| Contact Email: | kms@cs.stir.ac.uk |
| Title: | High capacity content addressable memory with mixed order hyper networks |
| Editor(s): | 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 J, Rosa A, Cadenas J, Correia A, Mandani K, Ruano A & Filipe J (eds.) Computational Intelligence: International Joint Conference, IJCCI 2015 Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Studies in Computational Intelligence, 669. Computational Intelligence International Joint Conference, IJCCI 2015, Lisbon, Portugal, 12.11.2015-14.11.2015. Cham, Switzerland: Springer, pp. 337-358. https://doi.org/10.1007/978-3-319-48506-5_17 |
| Issue Date: | 2017 |
| Date Deposited: | 1-Dec-2017 |
| Series/Report no.: | Studies in Computational Intelligence, 669 |
| Conference Name: | Computational Intelligence International Joint Conference, IJCCI 2015 |
| Conference Dates: | 2015-11-12 - 2015-11-14 |
| 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: | VoR - Version of Record |
| Rights: | The 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. |
| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Swingler_SCI669_2016.pdf | Fulltext - Published Version | 990.37 kB | Adobe PDF | Under Embargo until 2998-10-31 Request a copy |
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright |
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/
If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.