HIGHLIGHTS
SUMMARY
In the case of neural_networks, determining the optimal number of parameters (intermediate layers, hidden neurons, activation functions, etc.) requires time-consuming calculations. It can therefore be stated that SVM has better generalization capabilities than artificial neural_networks and generally provides better results. Obviously, this is a general theory, but depending on the available data and the problem, neural_networks can outperform support vector machines. Bizios et_al aimed to conduct a study comparing MLC-type algorithms: artificial neural_networks (ANN) and support vector machines (SVM). The algorithms used for solving the proposed problem are neural_networks with different topologies . . .
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