ITM Web of Conferences
Volume 6, 20166th Seminar on Industrial Control Systems: Analysis, Modeling and Computation
|Number of page(s)||4|
|Published online||25 March 2016|
Hybrid pre training algorithm of Deep Neural Networks
St. Petersburg State University, Faculty of applied mathematics and control processes, 199034 St. Petersburg, Russia
a Corresponding author: firstname.lastname@example.org
This paper proposes a hybrid algorithm of pre training deep networks, using both marked and unmarked data. The algorithm combines and extends the ideas of Self-Taught learning and pre training of neural networks approaches on the one hand, as well as supervised learning and transfer learning on the other. Thus, the algorithm tries to integrate in itself the advantages of each approach. The article gives some examples of applying of the algorithm, as well as its comparison with the classical approach to pre training of neural networks. These examples show the effectiveness of the proposed algorithm.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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