Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions
Titre:
Effective Statistical Learning Methods for Actuaries III Neural Networks and Extensions
ISBN (Numéro international normalisé des livres):
9783030258276
Auteur personnel:
Edition:
1st ed. 2019.
PRODUCTION_INFO:
Cham : Springer International Publishing : Imprint: Springer, 2019.
Description physique:
XIII, 250 p. 78 illus., 75 illus. in color. online resource.
Collections:
Springer Actuarial Lecture Notes,
Table des matières:
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.
Extrait:
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. .
Auteur collectif ajouté:
Langue:
Anglais