European University Institute Library

Effective Statistical Learning Methods for Actuaries III, Neural Networks and Extensions, by Michel Denuit, Donatien Hainaut, Julien Trufin

eng

0

non fiction

Effective Statistical Learning Methods for Actuaries III

electronic resource

dictionaries

1126541852

by Michel Denuit, Donatien Hainaut, Julien Trufin

Springer Actuarial Lecture Notes,, 2523-3289Springer eBooks.

Neural Networks and Extensions

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.--, Provided by publisher

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