Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF01414350
Title: A neural network construction algorithm with application to image compression
Authors: Setiono, R. 
Lu, G. 
Keywords: Backpropagation method
Feedforward neural network
Image compression
Neural network construction algorithm
Quasi-Newton method
Issue Date: Jun-1994
Source: Setiono, R.,Lu, G. (1994-06). A neural network construction algorithm with application to image compression. Neural Computing & Applications 2 (2) : 61-68. ScholarBank@NUS Repository. https://doi.org/10.1007/BF01414350
Abstract: We propose an algorithm for constructing a feedforward neural network with a single hidden layer. This algorithm is applied to image compression and it is shown to give satisfactory results. The neural network construction algorithm begins with a simple network topology containing a single unit in the hidden layer. An optimal set of weights for this network is obtained by applying a variant of the quasi-Newton method for unconstrained optimisation. If this set of weights does not give a network with the desired accuracy then one more unit is added to the hidden layer and the network is retrained. This process is repeated until the desired network is obtained. We show that each addition of the hidden unit to the network is guaranteed to increase the signal to noise ratio of the compressed image. © 1994 Springer-Verlag London Limited.
Source Title: Neural Computing & Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/99159
ISSN: 09410643
DOI: 10.1007/BF01414350
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