| LDR | | | 00000cab a2200000 4500 |
| 001 | | | MAP20260012308 |
| 003 | | | MAP |
| 005 | | | 20260422174136.0 |
| 008 | | | 260421e20260316esp|||p |0|||b|spa d |
| 040 | | | $aMAP$bspa$dMAP |
| 084 | | | $a6 |
| 100 | | | $0MAPA20260007359$aKim, Youngsun |
| 245 | 1 | 0 | $aTransfer learning in the actuarial domain$b: foundations and applications$cYoungsun Kim and Daniel Bauer |
| 520 | | | $aThe article systematically analyzes the use of transfer learning in the actuarial field, particularly in the prediction of claim frequency when labeled data are scarce. It presents formal definitions, typologies, and methodological approaches, linking them to classical concepts such as credibility theory. Through empirical applications in automobile insurance, instance-based, feature-based, and parameter-based methods are evaluated. The results show improvements in accuracy and stability compared to traditional models. The study positions transfer learning as a relevant tool for modern actuarial analysis |
| 650 | | 4 | $0MAPA20080579258$aCálculo actuarial |
| 650 | | 4 | $0MAPA20170005476$aMachine learning |
| 650 | | 4 | $0MAPA20080592059$aModelos predictivos |
| 650 | | 4 | $0MAPA20080603779$aSeguro de automóviles |
| 650 | | 4 | $0MAPA20080611200$aInteligencia artificial |
| 700 | 1 | | $0MAPA20080650254$aBauer, Daniel |
| 773 | 0 | | $wMAP20077000239$g16/03/2026 Tomo 30 Número 1 - 2026 , 23 p.$x1092-0277$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997- |
