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Effective statistical learning metho...
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Denuit, Michel.
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.
Contained By:
Springer eBooks
Subject:
Actuarial science.
Online resource:
https://doi.org/10.1007/978-3-030-25827-6
ISBN:
9783030258276$q(electronic bk.)
Effective statistical learning methods for actuaries.III,Neural networks and extensions
Denuit, Michel.
Effective statistical learning methods for actuaries.
III,Neural networks and extensions[electronic resource] /by Michel Denuit, Donatien Hainaut, Julien Trufin. - Cham :Springer International Publishing :2019. - xiii, 250 p. :ill. (some col.), digital ;24 cm. - Springer actuarial lecture notes,2523-3289. - Springer actuarial lecture notes..
Preface. - Feed-forward Neural Networks. - Byesian Neural Networks and GLM. - Deep Neural Networks -- Dimension-Reduction with Forward Neural Nets Applied to Mortality. - Self-organizing Maps and k-means clusterin in non Life Insurance. - Ensemble of Neural Networks -- Gradient Boosting with Neural Networks. - Time Series Modelling with Neural Networks -- References.
Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.
ISBN: 9783030258276$q(electronic bk.)
Standard No.: 10.1007/978-3-030-25827-6doiSubjects--Topical Terms:
603592
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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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.
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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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Mathematics and Statistics (Springer-11649)
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EB HG8781 .D415 2019 2019
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https://doi.org/10.1007/978-3-030-25827-6
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