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- W2983756685 abstract "In this chapter, we study a particular type of neural networks that are designed for providing a representation of the input with a reduced dimensionality. These networks contains a hidden layer, called bottleneck, that contains a few nodes compared to the previous layers. The output signals of neurons in the bottleneck carry a summarized information that aggregates input signals in a non-linear way. Bottleneck networks offer an interesting alternative to principal component analysis (PCA) or non-linear PCA. In actuarial sciences, these networks can be used for understanding the evolution of longevity during the last century. We also introduce in this chapter a genetic algorithm for calibrating the neural networks. This method combined with a gradient descent speeds up the calibration." @default.
- W2983756685 created "2019-11-22" @default.
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- W2983756685 date "2019-01-01" @default.
- W2983756685 modified "2023-09-26" @default.
- W2983756685 title "Dimension-Reduction with Forward Neural Nets Applied to Mortality" @default.
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- W2983756685 doi "https://doi.org/10.1007/978-3-030-25827-6_4" @default.
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