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

• Κύριοι συγγραφείς: Denuit, Michel (Συγγραφέας, 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)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Springer Actuarial Lecture Notes,
• Θέματα: 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...

Πίνακας περιεχομένων:

• 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.