標(biāo)題: Titlebook: Effective Statistical Learning Methods for Actuaries III; Neural Networks and Michel Denuit,Donatien Hainaut,Julien Trufin Textbook 2019 S [打印本頁] 作者: infection 時間: 2025-3-21 17:39
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書目名稱Effective Statistical Learning Methods for Actuaries III讀者反饋
書目名稱Effective Statistical Learning Methods for Actuaries III讀者反饋學(xué)科排名
作者: 抗生素 時間: 2025-3-21 20:57
Effective Statistical Learning Methods for Actuaries IIINeural Networks and 作者: cloture 時間: 2025-3-22 02:55
Matth?us Ebinal,Valery Mitjuschkin Shu and Burn (Water Resour Res 40:1–10, 2004) forecast flood frequencies with an ensemble of networks. We start this chapter by describing the bias-variance decomposition of the prediction error. Next, we discuss how aggregated models and randomized models reduce the prediction error by decreasing 作者: inquisitive 時間: 2025-3-22 06:41
Textbook 2019hods for Actuaries.. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently..作者: 挑剔為人 時間: 2025-3-22 09:15 作者: 出汗 時間: 2025-3-22 15:48 作者: 出汗 時間: 2025-3-22 20:16 作者: progestogen 時間: 2025-3-22 21:22
Dimension-Reduction with Forward Neural Nets Applied to Mortality,native 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.作者: 學(xué)術(shù)討論會 時間: 2025-3-23 02:47
Neues Selbstbild und Rollenprofilnt of our a priori knowledge about parameters based on Markov Chain Monte Carlo methods. In order to explain those methods that are based on simulations, we need to review the main features of Markov chains.作者: Engaging 時間: 2025-3-23 07:34
Lando Kirchmair,Daniel-Erasmus Khands of regularization for avoiding the overfitting. We next explain why deep neural networks outperform shallow networks for approximating hierarchical binary functions. This chapter is concluded by a numerical illustration.作者: 同步信息 時間: 2025-3-23 12:03 作者: 乞討 時間: 2025-3-23 14:30
Bayesian Neural Networks and GLM,nt of our a priori knowledge about parameters based on Markov Chain Monte Carlo methods. In order to explain those methods that are based on simulations, we need to review the main features of Markov chains.作者: MAG 時間: 2025-3-23 21:52 作者: 咽下 時間: 2025-3-24 01:09
Self-organizing Maps and k-Means Clustering in Non Life Insurance,curacy of the prediction. In this situation, the coefficient estimates of the multiple regression may change erratically in response to small changes in the model or the data. Self-organizing maps offer an elegant solution to segment explanatory variables and to detect dependence among covariates.作者: 過剩 時間: 2025-3-24 02:27
Textbook 2019neously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible...Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-for作者: garrulous 時間: 2025-3-24 06:36
2523-3262 udy.Features a rigorous statistical analysis of neural netwo.This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a s作者: Pruritus 時間: 2025-3-24 14:22 作者: Obstruction 時間: 2025-3-24 14:58
Das Rezidiv in der gyn?kologischen Onkologieward 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.作者: 沖擊力 時間: 2025-3-24 19:57
Neues Selbstbild und Rollenprofilwe 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. 作者: defray 時間: 2025-3-25 02:53 作者: 評論者 時間: 2025-3-25 07:03
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作者: muster 時間: 2025-3-25 10:59
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作者: Pathogen 時間: 2025-3-25 11:56 作者: 鄙視 時間: 2025-3-25 16:56
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作者: Ankylo- 時間: 2025-3-25 20:33
,Die ersten Anzeichen eines gro?en 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.作者: Infant 時間: 2025-3-26 01:34 作者: nominal 時間: 2025-3-26 05:14 作者: Perineum 時間: 2025-3-26 09:46 作者: Cardiac-Output 時間: 2025-3-26 14:27 作者: Malleable 時間: 2025-3-26 18:18 作者: 小丑 時間: 2025-3-27 00:52
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作者: 分散 時間: 2025-3-27 04:46
Springer Actuarialhttp://image.papertrans.cn/e/image/302812.jpg作者: 昆蟲 時間: 2025-3-27 09:18
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.作者: 外來 時間: 2025-3-27 10:13 作者: Cloudburst 時間: 2025-3-27 15:30
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.作者: tattle 時間: 2025-3-27 21:15
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. 作者: Etymology 時間: 2025-3-27 22:45 作者: 態(tài)學(xué) 時間: 2025-3-28 06:04
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作者: Insul島 時間: 2025-3-28 10:12 作者: 多余 時間: 2025-3-28 13:27 作者: 直覺沒有 時間: 2025-3-28 15:10
Gradient Boosting with Neural Networks,ications. 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
