LEADER 03732nam 22005895 450 001 996416847203316 005 20200701050518.0 010 $a3-030-25827-0 024 7 $a10.1007/978-3-030-25827-6 035 $a(CKB)4100000009759029 035 $a(MiAaPQ)EBC5969615 035 $a(DE-He213)978-3-030-25827-6 035 $a(PPN)258306408 035 $a(EXLCZ)994100000009759029 100 $a20191031d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEffective Statistical Learning Methods for Actuaries III$b[electronic resource] $eNeural Networks and Extensions /$fby Michel Denuit, Donatien Hainaut, Julien Trufin 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (258 pages) $cillustrations 225 1 $aSpringer Actuarial Lecture Notes,$x2523-3289 311 $a3-030-25826-2 327 $aPreface. - 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. 330 $aArtificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. . 410 0$aSpringer Actuarial Lecture Notes,$x2523-3289 606 $aActuarial science 606 $aStatistics  606 $aNeural networks (Computer science)  606 $aActuarial Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/M13080 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 615 0$aActuarial science. 615 0$aStatistics . 615 0$aNeural networks (Computer science) . 615 14$aActuarial Sciences. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a368.01 700 $aDenuit$b Michel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781288 702 $aHainaut$b Donatien$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTrufin$b Julien$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996416847203316 996 $aEffective Statistical Learning Methods for Actuaries III$92416322 997 $aUNISA