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Study of cost functions in three term backpropagation for classification problems

Three Term Backpropagation was proposed in 2003 by Zweiri, and has outperformed standard Two Term Backpropagation. However, further studies on Three Term Backpropagation in 2007 indicated that the network only surpassed standard BP for small scale datasets (below 100 instances) but not for medium an...

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Main Author: Kuppusamy, Puspadevi
Format: Thesis
Language:English
Published: 2008
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/9459/1/PuspadeviKuppusamyFSKSM2008.pdf
http://eprints.utm.my/id/eprint/9459/
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spelling my.utm.94592018-07-19T01:38:54Z http://eprints.utm.my/id/eprint/9459/ Study of cost functions in three term backpropagation for classification problems Kuppusamy, Puspadevi QA75 Electronic computers. Computer science Three Term Backpropagation was proposed in 2003 by Zweiri, and has outperformed standard Two Term Backpropagation. However, further studies on Three Term Backpropagation in 2007 indicated that the network only surpassed standard BP for small scale datasets (below 100 instances) but not for medium and large scale datasets (above 100 instances). It has also been observed that by using Mean Square Error (MSE) as a cost function in Three Term Backpropagation network, has some drawbacks such as incorrect saturation and tend to trap in local minima, resulting in slow convergence and poor performance. In this study, substantial experiments on implementing various cost functions on Three Term BP are executed to probe the effectiveness of this network. The performance is measured in terms of convergence time and accuracy. The costs functions involve in this study include Mean Square Error, Bernoulli function, Modified cost function and Improved cost function. These cost functions were introduced by previous researchers. The outcome indicates that MSE is not an ideal cost function to be used for Three Term BP. Besides that, the results have also illustrated that improve cost function’s converges faster, while modified cost function produces high accuracy in classification 2008-10 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/9459/1/PuspadeviKuppusamyFSKSM2008.pdf Kuppusamy, Puspadevi (2008) Study of cost functions in three term backpropagation for classification problems. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:686?site_name=Restricted Repository
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kuppusamy, Puspadevi
Study of cost functions in three term backpropagation for classification problems
description Three Term Backpropagation was proposed in 2003 by Zweiri, and has outperformed standard Two Term Backpropagation. However, further studies on Three Term Backpropagation in 2007 indicated that the network only surpassed standard BP for small scale datasets (below 100 instances) but not for medium and large scale datasets (above 100 instances). It has also been observed that by using Mean Square Error (MSE) as a cost function in Three Term Backpropagation network, has some drawbacks such as incorrect saturation and tend to trap in local minima, resulting in slow convergence and poor performance. In this study, substantial experiments on implementing various cost functions on Three Term BP are executed to probe the effectiveness of this network. The performance is measured in terms of convergence time and accuracy. The costs functions involve in this study include Mean Square Error, Bernoulli function, Modified cost function and Improved cost function. These cost functions were introduced by previous researchers. The outcome indicates that MSE is not an ideal cost function to be used for Three Term BP. Besides that, the results have also illustrated that improve cost function’s converges faster, while modified cost function produces high accuracy in classification
format Thesis
author Kuppusamy, Puspadevi
author_facet Kuppusamy, Puspadevi
author_sort Kuppusamy, Puspadevi
title Study of cost functions in three term backpropagation for classification problems
title_short Study of cost functions in three term backpropagation for classification problems
title_full Study of cost functions in three term backpropagation for classification problems
title_fullStr Study of cost functions in three term backpropagation for classification problems
title_full_unstemmed Study of cost functions in three term backpropagation for classification problems
title_sort study of cost functions in three term backpropagation for classification problems
publishDate 2008
url http://eprints.utm.my/id/eprint/9459/1/PuspadeviKuppusamyFSKSM2008.pdf
http://eprints.utm.my/id/eprint/9459/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:686?site_name=Restricted Repository
_version_ 1643645160950595584
score 13.211869

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