Gaussian kernel approximation algorithm for feedforward neural network design
A Gaussian kernel approximation algorithm for a feedforward neural network is presented. The approach used by the algorithm, which is based on a constructive learning algorithm, is to create the hidden units directly so that automatic design of the architecture of neural networks can be carried out....
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th-mahidol.277672018-09-13T13:47:34Z Gaussian kernel approximation algorithm for feedforward neural network design Ananta Srisuphab Jarernsri L. Mitrpanont Mahidol University Mathematics A Gaussian kernel approximation algorithm for a feedforward neural network is presented. The approach used by the algorithm, which is based on a constructive learning algorithm, is to create the hidden units directly so that automatic design of the architecture of neural networks can be carried out. The algorithm is defined using the linear summation of input patterns and their randomized input weights. Hidden-layer nodes are defined so as to partition the input space into homogeneous regions, where each region contains patterns belonging to the same class. The largest region is used to define the center of the corresponding Gaussian hidden nodes. The algorithm is tested on three benchmark data sets of different dimensionality and sample sizes to compare the approach presented here with other algorithms. Real medical diagnoses and a biological classification of mushrooms are used to illustrate the performance of the algorithm. These results confirm the effectiveness of the proposed algorithm. © 2009 Elsevier Inc. All rights reserved. 2018-09-13T06:47:34Z 2018-09-13T06:47:34Z 2009-12-01 Article Applied Mathematics and Computation. Vol.215, No.7 (2009), 2686-2693 10.1016/j.amc.2009.09.008 00963003 2-s2.0-70350721632 https://repository.li.mahidol.ac.th/handle/123456789/27767 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=70350721632&origin=inward |
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Mathematics Ananta Srisuphab Jarernsri L. Mitrpanont Gaussian kernel approximation algorithm for feedforward neural network design |
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A Gaussian kernel approximation algorithm for a feedforward neural network is presented. The approach used by the algorithm, which is based on a constructive learning algorithm, is to create the hidden units directly so that automatic design of the architecture of neural networks can be carried out. The algorithm is defined using the linear summation of input patterns and their randomized input weights. Hidden-layer nodes are defined so as to partition the input space into homogeneous regions, where each region contains patterns belonging to the same class. The largest region is used to define the center of the corresponding Gaussian hidden nodes. The algorithm is tested on three benchmark data sets of different dimensionality and sample sizes to compare the approach presented here with other algorithms. Real medical diagnoses and a biological classification of mushrooms are used to illustrate the performance of the algorithm. These results confirm the effectiveness of the proposed algorithm. © 2009 Elsevier Inc. All rights reserved. |
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Mahidol University |
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Mahidol University Ananta Srisuphab Jarernsri L. Mitrpanont |
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Ananta Srisuphab Jarernsri L. Mitrpanont |
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Ananta Srisuphab |
title |
Gaussian kernel approximation algorithm for feedforward neural network design |
title_short |
Gaussian kernel approximation algorithm for feedforward neural network design |
title_full |
Gaussian kernel approximation algorithm for feedforward neural network design |
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Gaussian kernel approximation algorithm for feedforward neural network design |
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Gaussian kernel approximation algorithm for feedforward neural network design |
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gaussian kernel approximation algorithm for feedforward neural network design |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/27767 |
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