Abstract
Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations. In the context of Neural Networks the term “learning” means that the knowledge acquired from the samples can be generalized to as yet unseen observations. In this sense, a Neural Network is often called a Learning Machine. As such, Neural Networks might be considered as a metaphor for an agent who learns dependencies of his environment and thus infers strategies of behavior based on a limited number of observations. In this contribution, however, we want to abstract from the biological origins of Neural Networks and rather present them as a purely mathematical model.
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Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P., Obermayer, K. (1999). Neural Networks in Economics. In: Brenner, T. (eds) Computational Techniques for Modelling Learning in Economics. Advances in Computational Economics, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5029-7_7
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