LEADER 03643nam 22006615 450 001 9910484306203321 005 20251113193346.0 010 $a9783030575564 010 $a303057556X 024 7 $a10.1007/978-3-030-57556-4 035 $a(CKB)4100000011586001 035 $a(DE-He213)978-3-030-57556-4 035 $a(MiAaPQ)EBC6455952 035 $a(PPN)252509145 035 $a(MiAaPQ)EBC31862527 035 $a(Au-PeEL)EBL31862527 035 $a(EXLCZ)994100000011586001 100 $a20201116d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEffective Statistical Learning Methods for Actuaries II $eTree-Based Methods and Extensions /$fby Michel Denuit, Donatien Hainaut, Julien Trufin 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (X, 228 p. 68 illus., 6 illus. in color.) 225 1 $aSpringer Actuarial Lecture Notes,$x2523-3297 311 08$a9783030575557 311 08$a3030575551 327 $aChapter 1: Introductio -- Chapter 2 : Performance Evaluation -- Chapter 3 Regression Trees -- Chapter 4 Bagging Trees and Random Forests -- Chapter 5 Boosting Trees -- Chapter 6 Other Measures for Model Comparison. 330 $aThis book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, masters students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. 410 0$aSpringer Actuarial Lecture Notes,$x2523-3297 606 $aActuarial science 606 $aNeural networks (Computer science) 606 $aStatistics 606 $aActuarial Mathematics 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aActuarial science. 615 0$aNeural networks (Computer science) 615 0$aStatistics. 615 14$aActuarial Mathematics. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a519.536 700 $aDenuit$b Michel$0781288 702 $aHainaut$b Donatien 702 $aTrufin$b Julien 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484306203321 996 $aEffective Statistical Learning Methods for Actuaries II$94464288 997 $aUNINA LEADER 03631nam 22007815 450 001 9910299702303321 005 20260415134350.0 010 $a9783662455142 010 $a3662455145 024 7 $a10.1007/978-3-662-45514-2 035 $a(CKB)3710000000337853 035 $a(EBL)1966137 035 $a(OCoLC)900193757 035 $a(SSID)ssj0001424377 035 $a(PQKBManifestationID)11832250 035 $a(PQKBTitleCode)TC0001424377 035 $a(PQKBWorkID)11367216 035 $a(PQKB)11019799 035 $a(DE-He213)978-3-662-45514-2 035 $a(MiAaPQ)EBC1966137 035 $a(PPN)183517547 035 $a(EXLCZ)993710000000337853 100 $a20150112d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Vision and Mechatronics in Practice /$fedited by John Billingsley, Peter Brett 205 $a1st ed. 2015. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2015. 215 $a1 online resource (343 p.) 300 $aDescription based upon print version of record. 311 08$a9783662455135 311 08$a3662455137 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aMining -- Surgery -- Quadrucopters -- Manipulators -- Mobile applications -- Sensing and control -- Education -- Manufacturing -- Other. 330 $aThe contributions for this book have been gathered over several years from conferences held in the series of Mechatronics and Machine Vision in Practice, the latest of which was held in Ankara, Turkey. The essential aspect is that they concern practical applications rather than the derivation of mere theory, though simulations and visualization are important components. The topics range from mining, with its heavy engineering, to the delicate machining of holes in the human skull or robots for surgery on human flesh. Mobile robots continue to be a hot topic, both from the need for navigation and for the task of stabilization of unmanned aerial vehicles. The swinging of a spray rig is damped, while machine vision is used for the control of heating in an asphalt-laying machine.  Manipulators are featured, both for general tasks and in the form of grasping fingers. A robot arm is proposed for adding to the mobility scooter of the elderly. Can EEG signals be a means to control a robot? Can face recognition be achieved in varying illumination?"  . 606 $aAutomatic control 606 $aRobotics 606 $aAutomation 606 $aComputer vision 606 $aSignal processing 606 $aArtificial intelligence 606 $aControl, Robotics, Automation 606 $aComputer Vision 606 $aSignal, Speech and Image Processing 606 $aArtificial Intelligence 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aAutomation. 615 0$aComputer vision. 615 0$aSignal processing. 615 0$aArtificial intelligence. 615 14$aControl, Robotics, Automation. 615 24$aComputer Vision. 615 24$aSignal, Speech and Image Processing. 615 24$aArtificial Intelligence. 676 $a006.3 676 $a006.37 676 $a006.6 676 $a620 702 $aBillingsley$b J$g(John),$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBrett$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299702303321 996 $aMachine Vision and Mechatronics in Practice$91466053 997 $aUNINA