Fine-Tuning a Neural Network on a Given Database
Main Article Content
Abstract
Several versions of neural networks are now available, which are being used in practice in more and more places when we are looking for correlations in larger databases. Among computer applications, many variants of artificial neural networks (NN for short) are used in everyday applications. Examples include customer rating, healthcare, or even data mining. Of course, different neural network types and algorithms are used in image and sound and text processing or machine translation, and others in simple data mining, or correlation search and data analysis. We have tested our system with a graphical interface developed in the python script language on several real and generated databases of different sizes, with success so far. The purpose of this analysis is how to improve the accuracy of the prediction by changing the individual parameters (number of neurons, number of iterations, etc.) and thus how to increase the accuracy of the network when analyzing a given database.
Downloads
Article Details
References
Altrichter M., Horváth G., Pataki B., Strausz Gy., Takács G., Valyon J. (2007): Neurális hálózatok. Panem Könyvkiadó. Budapest.
Chen, G. (2016): A gentle tutorial of recurrent neural network with error backpropagation. arXiv preprint, arXiv:1610.02583. https://doi.org/10.48550/arXiv.1610.02583
Chollet, F. (2021): Deep learning with Python. Simon and Schuster. <https://www.simonandschuster.com/books/Deep-Learning-with-Python/Francois-Chollet/9781617294433> (2022.10.10.)
Fazekas I. (2013): Neurális hálózatok. Debreceni Egyetem Informatikai Kar. Debrecen.
Freedman, R. S. (2019): Visual Backpropagation. arXiv preprint, arXiv:1906.04011.
https://doi.org/10.48550/arXiv.1906.04011
Füvesi V., Konyha J. (2016). Gépi tanulást segítő függvénykönyvtárak áttekintése. Review of machine learning toolboxes. Műszaki Tudomány az Észak – Kelet Magyarországi Régióban.
Lubicz, M., Pawelczyk, K., Rzechonek, A., Kolodziej, J. (2013). Thoracic Surgery Data. UCI Machine Learning Repository. <https://archive-beta.ics.uci.edu/> (2022.10.10.)
Raschka, S., Mirjalili, V. (2019): Python Machine Learning - Third Edition. Packt Publishing. <https://github.com/packtpublishing/python-machine-learning> (2022.10.05.)
Tan, P-N., Steinbach, M., Kumar, V. (2006). Bevezetés az adatbányászatba - Introduction to Data Mining. Panem Könyvkiadó, Budapest.
Tóth L., Grósz T. (2017): Mesterséges Neuronhálók és alkalmazásaik. Szegedi Tudományegyetem, Szeged. <https://www.inf.u-szeged.hu/~tothl/ann/Neuronhalok-egyben.pdf> (2022.09.15.)