Národní úložiště šedé literatury Nalezeno 1 záznamů.  Hledání trvalo 0.00 vteřin. 
Imbalanced data training approaches in neural network
Vicianová, Veronika ; Ředina, Richard (oponent) ; Jakubíček, Roman (vedoucí práce)
This thesis deals with the research and implementation of methods that eliminate the influence of an imbalanced dataset on the learning of neural networks. Individual methods are compared with each other for different levels of imbalance. The experiments carried out in the work are also compared with the available literature and a control experiment, which was carried out without the method of eliminating the influence of an imbalanced dataset. The experiments are extended to another dataset containing the original imbalance and compared. In the theoretical section, the topic of neural networks and the problems that may occur during learning are brought up. Subsequently, convolutional networks and their optimization algorithms are presented. The thesis also contains a more detailed presentation of the issue of an imbalanced dataset, including the metrics used in experiments and approaches used to eliminate this problem.

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