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

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https://doi.org/10.1007/978-3-658-40018-7lity. 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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https://doi.org/10.1007/978-3-322-84288-6ow the desired outputs for combinations of these variables. For example, forecasting the frequency of car accidents with a perceptron requires an a priori segmentation of some explanatory variables like the driver’s age into categories, in a similar manner to Generalized Linear Models. The misspecif

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https://doi.org/10.1007/978-3-322-84171-1ications. Ensemble techniques rely on simple averaging of models in the ensemble. The family of boosting methods adopts a different strategy to construct ensembles. In boosting algorithms, new models are sequentially added to the ensemble. At each iteration, a new weak base-learner is trained with r

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,Die ersten Anzeichen eines großen Problems,Time series modelling may be applied in many different fields. In finance, it is used for explaining the evolution of asset returns. In actuarial sciences, it may be used for forecasting the number of claims caused by natural phenomenons or for claims reserving.

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