Titlebook: Effective Statistical Learning Methods for Actuaries III; Neural Networks and Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2019 S

小丑

Michel Denuit,Donatien Hainaut,Julien TrufinProvides an exhaustive and self-contained presentation of neural networks applied to insurance.Can be used as course material or for self-study.Features a rigorous statistical analysis of neural netwo

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Springer Actuarialhttp://image.papertrans.cn/e/image/302812.jpg

昆蟲

Feed-Forward Neural Networks,ward 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.

外來

Cloudburst

Feed-Forward Neural Networks,ward 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.

tattle

Bayesian Neural Networks and GLM,we cannot rely anymore on asymptotic properties of maximum likelihood estimators to approximate confidence intervals. Applying the Bayesian learning paradigm to neural networks or to generalized linear models results in a powerful framework that can be used for estimating the density of predictors.

Etymology

態(tài)學(xué)

Dimension-Reduction with Forward Neural Nets Applied to Mortality,lity. 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 alter

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