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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Effective Statistical Learning Methods for Actuaries III/ by Michel Denuit, Donatien Hainaut, Julien Trufin.
Reminder of title:	Neural Networks and Extensions /
Author:	Denuit, Michel.
other author:	Hainaut, Donatien.
Description:	XIII, 250 p. 78 illus., 75 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Actuarial science. -
Online resource:	https://doi.org/10.1007/978-3-030-25827-6
ISBN:	9783030258276

Effective Statistical Learning Methods for Actuaries III = Neural Networks and Extensions /
Denuit, Michel.

Effective Statistical Learning Methods for Actuaries IIINeural Networks and Extensions /[electronic resource] :by Michel Denuit, Donatien Hainaut, Julien Trufin. - 1st ed. 2019. - XIII, 250 p. 78 illus., 75 illus. in color.online resource. - Springer Actuarial Lecture Notes,2523-3289. - Springer Actuarial Lecture Notes,.

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.

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. .

ISBN: 9783030258276

Standard No.: 10.1007/978-3-030-25827-6doiSubjects--Topical Terms:

943795
Actuarial science.

LC Class. No.: HG8779-8793

Dewey Class. No.: 368.01

Effective Statistical Learning Methods for Actuaries III = Neural Networks and Extensions /
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520 $a 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. .
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