Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions /

• Main Authors: Denuit, Michel (Author, http://id.loc.gov/vocabulary/relators/aut), Hainaut, Donatien (http://id.loc.gov/vocabulary/relators/aut), Trufin, Julien (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Springer Actuarial Lecture Notes,
• Subjects: Actuarial science.; Statistics .; Neural networks (Computer science) .; Actuarial Sciences.; Statistics for Business, Management, Economics, Finance, Insurance.; Mathematical Models of Cognitive Processes and Neural Networks.

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 simultaneousl...

Table of Contents:

• 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.