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Transfer Learning in GMDH-Type Neural Networks

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Multimedia and Network Information Systems (MISSI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 833))

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Abstract

Recognition of difficult patterns with the accuracy comparable to that of the human brain is a challenging problem. The ability of the human to excel at this task has motivated the use of Artificial Neural Networks (ANNs) which under certain conditions provide efficient solutions. ANNs are still unable to use the full potential of modular and holistic operations of biological neurons and their networks. The ability of neurons to transfer learned behaviour has inspired an idea to train ANN for a new task by using the behaviour patterns learnt from a related task. The useful patterns transferred from one task to another can significantly reduce the time needed to learn new patterns, and gives the neurons the ability to generalise instead of memorising patterns. In this paper we explore the ability of transfer learning for a face recognition problem by using Group Method of Data Handling (GMDH) type of Deep Neural Networks. In our experiments we show that the transfer learning of a GMDH-type neural network has reduced the training time by 31% on a face recognition benchmark.

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Acknowledgements

The authors would like to thank Dr Livija Jakaite, a member of the supervisory team at the School of Computer Science of University of Bedfordshire, for useful and constructive comments.

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Authors and Affiliations

  1. Department of Computer Science, Federal University Dutse, Dutse, Nigeria

    Aminu Abdullahi

  2. School of Computer Science, University of Bedfordshire, Luton, UK

    Mukti Akter

Authors
  1. Aminu Abdullahi
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  2. Mukti Akter
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Corresponding author

Correspondence to Mukti Akter .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Kazimierz ChoroÅ›

  2. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Marek Kopel

  3. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Elżbieta Kukla

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Siemiński

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Abdullahi, A., Akter, M. (2019). Transfer Learning in GMDH-Type Neural Networks. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds) Multimedia and Network Information Systems. MISSI 2018. Advances in Intelligent Systems and Computing, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-319-98678-4_18

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