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2018 | 59(2) Informatyka | 60-71

Article title

The Study of the Influence of Architecture on Effectiveness of Deep Neural Networks Training

Content

Title variants

PL
Badania wpływu architektury na skuteczność uczenia głębokich sieci neuronowych

Languages of publication

EN PL

Abstracts

EN
Paper presents impact of the neural network architecture on the training effectiveness and training time. Selected network architectures and training algorithm are described. Presented experimental results of research confirming the significant influence of architecture on the success of network training.
PL
W artykule przedstawiono wpływ architektury sieci neuronowej na skuteczność i czas uczenia sieci. Opisano wybrane architektury sieci, algorytm uczenia oraz zaprezentowano wyniki badań potwierdzających znaczący wpływ architektury na sukces uczenia sieci.

Year

Pages

60-71

Physical description

Contributors

  • Wyższa Szkoła Informatyki i Zarządzania w Rzeszowie
  • Wyższa Szkoła Informatyki i Zarządzania w Rzeszowie

References

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  • Hang S. et al. (2013), Error back propagation for sequence training of context-dependent deep networks for conversational speech transcription, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
  • Hunter D., Yu H., Pukish M.S., Kolbusz J., and Wilamowski B.M. (2012), Selection of Proper Neural Network Sizes and Architectures – A Comparative Study, “IEEE Trans. on Industrial Informatics”, Vol. 8.
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  • Wilamowski B.M., Hunter D., Malinowski A. (2003), Solving parity-N problems with feedforward neural networks, Proc. 2003 IEEE IJCNN.
  • Wilamowski B.M., Yu H. (2010a), Neural Network Learning Without Backpropagation, “IEEE Trans. on Neural Networks”, Vol. 21, No.11.
  • Wilamowski B.M., Yu H. (2010b), Improved Computation for Levenberg Marquardt Training, “IEEE Trans. on Neural Networks”, Vol. 21, No. 6.
  • Wilamowski B.M., Yu H. (2017), NNT – Neural Networks Trainer, http://www.eng.auburn.edu/ wilambm/nnt/ [access: 15.09.2017]
  • Xie T., Yu H., Hewlett J., Rozycki P., Wilamowski B.M. (2012), Fast and Efficient Second-Order Method for Training Radial Basis Function Networks, “IEEE Trans. on Neural Networks and Learning Systems”, Vol. 23, No. 4.
  • Yu H., Reiner P., Xie T., Bartczak T., Wilamowski B.M. (2014), An Incremental Design of Radial Basis Function Networks, “IEEE Transactions on Neural Networks and Learning Systems”, Vol. 25, No. 10.
  • Yu H., Xie T., Hewlett J., Rozycki P., Wilamowski B.M. (2012), Fast and Efficient Second Order Method for Training Radial Basis Function Networks, “IEEE Transactions on Neural Networks”, Vol. 24, Iss. 4.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-ee9afa13-8357-451a-a174-c2c38b578264
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