摘要： 介绍了一种基于生理年龄的精算定价新方式,该方式基于手背纹理照片,利用深度学习技术获得可靠的生理年龄评价结果,从而将其应用于保险上以获得更能反映投保人风险的定价。该技术和框架是深度学习在保险公司应用上的尝试,变革了数百年来保险公司基于日历年龄定价的传统模式。
| [1] GLEI D A,GOLDMAN N,RISQUES R A,et al.Predicting Sur-vival from Telomere Length versus Conventional Predictors:A Multinational Population-Based Cohort Study[J].Plos One,2016,1(4):e0152486. [2] LEVINE M E.Modeling the rate of senescence:can estimated biological age predict mortality more accurately than chronological age?[J].The journals of gerontology.Series A,Biological sciences and medical sciences,2013,8(6):667-674. [3] HILTON G E,SALAKHUTDINOV R R.Reducing the Dimensionality of Data with Neural Network[J].Science,2006,313:504-507. [4] LECUN Y,BENGIO Y,HILTON G.Deep Learning[J].Na-ture,2015,521:436-444. [5] KRIZHEVSKY A,SUTSKEVER I,H INTON G.ImageNet clas-sification with deep convolutional neural networks[C]∥Proc.Advances in Neural Information Processing Systems.2012,25:1090-1098. [6] TOMPSON J,JAIN A,LECUN Y,et al.Joint training of a convolutional network and a graphical model for human pose estimation[C]∥Proc.Advances in Neural Information Processing Systems.2014,27:1799-1807. [7] MIKOLOV T,DEORAS A,POVEY D,et al.Strategies fortraining large scale neural network language models[C]∥Proc.Automatic Speech Recognition and Understanding.2011:196-201. [8] MA J,SHERIDAN R P,LIAW A,et al.Deep neural nets as a method for quantitative structure-activity relationships[J].Journal of Chemical Information & Modeling,2015,5(2):263-274. [9] WESTON J,BORDES A,C HOPRA S,et al.Towards AI-complete question answering:a set of prerequisite toy tasks.http://arxiv.org/abs/1502.05698. [10] MNIH V,et al.Human-level control through deep reinforce-ment learning[J].Nature,2015,518:529-533. |
| No related articles found! |
|
||
首页