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D. Shi
Intelligent System Laboratory, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singapore 639798
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Mathematics

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Entropy Learning in Neural Network Geok See Ng; D. Shi; A. Wahab; H. Singh
ASEAN Journal on Science and Technology for Development Vol. 20 No. 3-4 (2003): ASEAN Journal on Science and Technology for Development (AJSTD)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.167 KB) | DOI: 10.29037/ajstd.362

Abstract

In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.
Co-Authors A. Wahab Geok See Ng H. Singh