Abstract
This chapter introduces the general features of artificial neural networks. After a presentation of the mathematical neural cell, we focus on feed-forward networks. First, we discuss the preprocessing of data and next we present a survey of the different methods for calibrating such networks. Finally, we apply the theory to an insurance data set and compare the predictive power of neural networks and generalized linear models.
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Notes
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A perceptron is a single artificial neuron using the Heaviside step function as the activation function. It was developed by Rosenblatt (1958) for image recognition.
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Denuit, M., Hainaut, D., Trufin, J. (2019). Feed-Forward Neural Networks. In: Effective Statistical Learning Methods for Actuaries III. Springer Actuarial(). Springer, Cham. https://doi.org/10.1007/978-3-030-25827-6_1
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DOI: https://doi.org/10.1007/978-3-030-25827-6_1
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