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Effective statistical learning methods for actuaries. III, Neural networks and extensions
- 作者： Denuit, Michel, author.
- 其他作者：
- 其他題名：
- Springer actuarial lecture notes.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Springer actuarial lecture notes,
- 主題： Actuarial science. , Insurance--Statistical methods. , Neural networks (Computer science) , Actuarial Sciences. , Statistics for Business, Management, Economics, Finance, Insurance. , Mathematical Models of Cognitive Processes and Neural Networks.
- ISBN： 9783030258276 (electronic bk.) 、 9783030258269 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Preface. - Feed-forward Neural Networks. - Byesian Neural Networks and GLM. - Deep Neural Networks -- Dimension-Reduction with Forward Neural Nets Applied to Mortality. - Self-organizing Maps and k-means clusterin in non Life Insurance. - Ensemble of Neural Networks -- Gradient Boosting with Neural Networks. - Time Series Modelling with Neural Networks -- References.
- 摘要註: Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.
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讀者標籤：
- 系統號： 005466603 | 機讀編目格式
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