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Open AccessArticle

Idea of Using Blockchain Technique for Choosing the Best Configuration of Weights in Neural Networks

Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland
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Algorithms 2019, 12(8), 163; https://doi.org/10.3390/a12080163
Received: 4 July 2019 / Revised: 1 August 2019 / Accepted: 6 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Algorithms for Pattern Recognition)
The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights. View Full-Text
Keywords: algorithms; blockchain; machine learning algorithms; blockchain; machine learning
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MDPI and ACS Style

Winnicka, A.; Kęsik, K. Idea of Using Blockchain Technique for Choosing the Best Configuration of Weights in Neural Networks. Algorithms 2019, 12, 163. https://doi.org/10.3390/a12080163

AMA Style

Winnicka A, Kęsik K. Idea of Using Blockchain Technique for Choosing the Best Configuration of Weights in Neural Networks. Algorithms. 2019; 12(8):163. https://doi.org/10.3390/a12080163

Chicago/Turabian Style

Winnicka, Alicja; Kęsik, Karolina. 2019. "Idea of Using Blockchain Technique for Choosing the Best Configuration of Weights in Neural Networks" Algorithms 12, no. 8: 163. https://doi.org/10.3390/a12080163

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