Abstract
Neural networks, also called neural nets, have the smallest operational applications compared to supervised machine learning techniques. These applications are seen in financial applications such as bankruptcy prediction, commodity trading, and detecting fraud in credit card and monetary transactions. They were developed to imitate the function of the human brain. Neural networks are more flexible than traditional nonlinear models and can be used for classification or prediction. No function form is required, nor is there a need to specify a model. They are universal approximators meaning given enough neurons and processing time; we can model any relationship between the input and output variable to any degree of precision, making them ideal for predictive modeling. But this does not mean that they are universally best all the time.
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Halawi, L., Clarke, A., George, K. (2022). Neural Networks. In: Harnessing the Power of Analytics. Springer, Cham. https://doi.org/10.1007/978-3-030-89712-3_7
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DOI: https://doi.org/10.1007/978-3-030-89712-3_7
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