UID:
almafu_9959338378502883
Umfang:
1 online resource (258 pages) :
,
illustrations
Ausgabe:
1st ed. 2019.
ISBN:
9783030258276
,
3030258270
Serie:
Springer Actuarial Lecture Notes,
Inhalt:
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. .
Anmerkung:
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.
Weitere Ausg.:
ISBN 9783030258269
Weitere Ausg.:
ISBN 3030258262
Sprache:
Englisch
DOI:
10.1007/978-3-030-25827-6
Bookmarklink
