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Comparison of Bayesian learning and conjugate gradient descent training of neural networks
Dissertation (MEng (Electronics))--University of Pretoria, 2001.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613503503728640 |
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| access_status_str | Open Access |
| author2 | Nortje, Willem Daniel |
| author_browse | Nortje, Willem Daniel |
| author_facet | Nortje, Willem Daniel |
| collection | Thesis |
| dc_rights_str_mv | © 2001, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Electronics))--University of Pretoria, 2001. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/29327 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:11.117Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/29327 Comparison of Bayesian learning and conjugate gradient descent training of neural networks Nortje, Willem Daniel Neural networks Bayesian neural networks Sampled optimisation Bayesian learning UCTD Dissertation (MEng (Electronics))--University of Pretoria, 2001. Neural networks are used in various fields to make predictions about the future value of a time series, or about the class membership of a given object. For the network to be effective, it needs to be trained on a set of training data combined with the expected results. Two aspects to keep in mind when considering a neural network as a solution, are the required training time and the prediction accuracy. This research compares the classification accuracy of conjugate gradient descent neural networks and Bayesian learning neural networks. Conjugate gradient descent networks are known for their short training times, but are not very consistent and results are heavily dependant on initial training conditions. Bayesian networks are slower, but much more consistent. The two types of neural networks are compared, and some attempts are made to combine their strong points in order to achieve shorter training times while maintaining a high classification accuracy. Bayesian learning outperforms the gradient descent methods by almost 1%, while the hybrid method achieves results between those of Bayesian learning and gradient descent. The drawback of the hybrid method is that there is no speed improvement above that of Bayesian learning. Electrical, Electronic and Computer Engineering unrestricted 2013-09-07T15:25:23Z 2004-11-09 2013-09-07T15:25:23Z 2001-10-28 2001 2004-11-09 Dissertation Nortje, W 2001, Comparison of Bayesian learning and conjugate gradient descent training of neural networks, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/29327 > http://hdl.handle.net/2263/29327 http://upetd.up.ac.za/thesis/available/etd-11092004-091241/ © 2001, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Neural networks Bayesian neural networks Sampled optimisation Bayesian learning UCTD Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title | Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title_full | Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title_fullStr | Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title_full_unstemmed | Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title_short | Comparison of Bayesian learning and conjugate gradient descent training of neural networks |
| title_sort | comparison of bayesian learning and conjugate gradient descent training of neural networks |
| topic | Neural networks Bayesian neural networks Sampled optimisation Bayesian learning UCTD |
| url | http://hdl.handle.net/2263/29327 http://upetd.up.ac.za/thesis/available/etd-11092004-091241/ |