Improving generalization of neural network using length as discriminant

This paper discusses the empirical evaluation of improving generalization performance of neural networks by systematic treatment of training and test failures. As a result of systematic treatment of failures, a discrimination technique using LENGTH was developed. The experiments presented in this pa...

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Main Authors: Siraj, Fadzilah, Partridge, Derek
Format: Article
Language:English
Published: Universiti Utara Malaysia 1999
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Online Access:http://repo.uum.edu.my/90/1/Fadzilah_Siraj.pdf
http://repo.uum.edu.my/90/
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spelling my.uum.repo.902010-07-04T01:52:11Z http://repo.uum.edu.my/90/ Improving generalization of neural network using length as discriminant Siraj, Fadzilah Partridge, Derek QA76 Computer software This paper discusses the empirical evaluation of improving generalization performance of neural networks by systematic treatment of training and test failures. As a result of systematic treatment of failures, a discrimination technique using LENGTH was developed. The experiments presented in this paper illustrate the application of discrimination technique using LENGTH to neural networks trained to solve supervised learning tasks such as the Launch Interceptor Condition 1 problem. The discriminant LENGTH is used to discriminate between the predicted "hard-to-learn" and predicted "easy-to-learn" patterns before these patterns are fed into the networks. The experimental results reveal that the utilization of LENGTH as discriminant has improved the average generalization of the networks increased. Universiti Utara Malaysia 1999 Article PeerReviewed application/pdf en http://repo.uum.edu.my/90/1/Fadzilah_Siraj.pdf Siraj, Fadzilah and Partridge, Derek (1999) Improving generalization of neural network using length as discriminant. Analisis, 6 (1&2). pp. 75-87. ISSN 0127-8983 http://ijms.uum.edu.my
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Siraj, Fadzilah
Partridge, Derek
Improving generalization of neural network using length as discriminant
description This paper discusses the empirical evaluation of improving generalization performance of neural networks by systematic treatment of training and test failures. As a result of systematic treatment of failures, a discrimination technique using LENGTH was developed. The experiments presented in this paper illustrate the application of discrimination technique using LENGTH to neural networks trained to solve supervised learning tasks such as the Launch Interceptor Condition 1 problem. The discriminant LENGTH is used to discriminate between the predicted "hard-to-learn" and predicted "easy-to-learn" patterns before these patterns are fed into the networks. The experimental results reveal that the utilization of LENGTH as discriminant has improved the average generalization of the networks increased.
format Article
author Siraj, Fadzilah
Partridge, Derek
author_facet Siraj, Fadzilah
Partridge, Derek
author_sort Siraj, Fadzilah
title Improving generalization of neural network using length as discriminant
title_short Improving generalization of neural network using length as discriminant
title_full Improving generalization of neural network using length as discriminant
title_fullStr Improving generalization of neural network using length as discriminant
title_full_unstemmed Improving generalization of neural network using length as discriminant
title_sort improving generalization of neural network using length as discriminant
publisher Universiti Utara Malaysia
publishDate 1999
url http://repo.uum.edu.my/90/1/Fadzilah_Siraj.pdf
http://repo.uum.edu.my/90/
http://ijms.uum.edu.my
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score 13.144533