Application of neural networks and support vector machines in coding theory and practice

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dc.contributor.author Berber, Stevan en
dc.contributor.author Kao, J en
dc.date.accessioned 2017-08-01T22:31:14Z en
dc.date.issued 2011 en
dc.identifier.citation In Support Vector Machines: Data Analysis, Machine Learning and Applications. 131-152. Nova Science Publishers, Inc., New York, N.Y. 2011 en
dc.identifier.isbn 9781612093420 en
dc.identifier.uri http://hdl.handle.net/2292/34669 en
dc.description.abstract In this chapter a mathematical model of a K'/n rate conventional convolutional encoder/decoder system was developed to be applied for decoding based on the gradient descent algorithm. For the system a general expression for the noise energy function, which is required for the recurrent neural networks decoding, is derived. The derivative is based on the representation of the encoding procedure as a mapping of a K'-dimensional message into n-dimensional Euclidean encoded bit set vector. The universal nature of derivative is demonstrated through its application for particular cases of a general 1/n rate code, a 1/2 encoder, and a 2/3 rate encoder. In order to eliminate the local minimum problem presented in the recurrent neural network, another global optimisation technique called support vector machine is investigated. Preliminary simulation results have been carried out, showing its potential to be applied as an alternative method to decode convolutional codes. en
dc.description.uri http://librarysearch.auckland.ac.nz/UOA2_A:Combined_Local:uoa_alma21183902340002091 en
dc.publisher Nova Science Publishers, Inc. en
dc.relation.ispartof Support Vector Machines: Data Analysis, Machine Learning and Applications en
dc.relation.ispartofseries Computer Science, Technology and Applications en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Application of neural networks and support vector machines in coding theory and practice en
dc.type Book Item en
pubs.begin-page 131 en
dc.rights.holder Copyright: Nova Science Publishers, Inc. en
pubs.end-page 152 en
pubs.place-of-publication New York, N.Y. en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.elements-id 425510 en
pubs.org-id Engineering en
pubs.org-id Department of Electrical, Computer and Software Engineering en
pubs.number 8 en
pubs.record-created-at-source-date 2017-08-02 en


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