Conference Paper

Opposite Transfer Functions and Backpropagation Through Time

Department of Systems Design Engineering, University of Waterloo, Ватерлоо, Ontario, Canada
DOI: 10.1109/FOCI.2007.371529 Conference: Proceedings of the IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, part of the IEEE Symposium Series on Computational Intelligence 2007, Honolulu, Hawaii, USA, 1-5 April 2007
Source: DBLP


Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements

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Available from: Hamid R. Tizhoosh, May 10, 2014
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    • "As a potential future work, one may also investigate the incorporation of the new barcode technology [18] into retrievaloriented classification combined with optimization techniques that employ the concept of opposite entities [25], [26], [27]. "
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    • "In order to implement a complete solution, we use LBP features and SVM to classify the images. One can, in future works, investigate the use of opposites as already reported in iterative for learning and optimization in order to see the effect of using a network incorporating opposites [46], [47], [48]. "
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    • "The results demonstrate that ORL outperforms the reinforcement learning. Similarly, by considering the opposite transfer functions and opposite weights, the opposition-based neural networks were also proposed to improve their learning speed and accuracy [23]– [25]. "
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