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Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training Algorithms For Image Compression Using MLP
Devesh Batra
Pages - 412 - 422     |    Revised - 07-10-2014     |    Published - 10-11-2014
Volume - 8   Issue - 6    |    Publication Date - November / December 2014  Table of Contents
Image Compression, Artificial Neural Network, Multilayer Perceptron, Training, Levenberg-Marquardt, Scaled Conjugate Gradient, Complexity.
The Internet paved way for information sharing all over the world decades ago and its popularity for distribution of data has spread like a wildfire ever since. Data in the form of images, sounds, animations and videos is gaining users’ preference in comparison to plain text all across the globe. Despite unprecedented progress in the fields of data storage, computing speed and data transmission speed, the demands of available data and its size (due to the increase in both, quality and quantity) continue to overpower the supply of resources. One of the reasons for this may be how the uncompressed data is compressed in order to send it across the network. This paper compares the two most widely used training algorithms for multilayer perceptron (MLP) image compression – the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient algorithm. We test the performance of the two training algorithms by compressing the standard test image (Lena or Lenna) in terms of accuracy and speed. Based on our results, we conclude that both algorithms were comparable in terms of speed and accuracy. However, the Levenberg- Marquardt algorithm has shown slightly better performance in terms of accuracy (as found in the average training accuracy and mean squared error), whereas the Scaled Conjugate Gradient algorithm faired better in terms of speed (as found in the average training iteration) on a simple MLP structure (2 hidden layers).
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Mr. Devesh Batra
Stanford University - India

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