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Effective statistical learning metho...

Denuit, Michel.

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Effective statistical learning methods for actuaries.. III,. Neural networks and extensions

Record Type:	Electronic resources : Monograph/item
Title/Author:	Effective statistical learning methods for actuaries./ by Michel Denuit, Donatien Hainaut, Julien Trufin.
Author:	Denuit, Michel.
other author:	Hainaut, Donatien.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiii, 250 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
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.III,Neural networks and extensions[electronic resource] /by Michel Denuit, Donatien Hainaut, Julien Trufin. - Cham :Springer International Publishing :2019. - xiii, 250 p. :ill. (some col.), digital ;24 cm. - 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:

1536028
Actuarial science.

LC Class. No.: HG8781 / .D468 2019

Dewey Class. No.: 368.01

Effective statistical learning methods for actuaries.. III,. Neural networks and extensions
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505 0 $a 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.
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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