[1] WANG Yiyi.Research summary on quantitative analysis of anti-terrorism research in China[J].Journal of Intelligence,2017,36(11):23-27.(in Chinese) 王一伊.我国反恐问题定量分析研究综述[J].情报杂志,2017,36(11):23-27. [2] WILLIS H H,MORRAL A R,KELLY T K,et al.Estimating terrorism risk[M].Santa Monica,USA:Rand Corporation,2006. [3] EZELL B C,BENNETT S P,VON WINTERFELDT D,et al.Probabilistic risk analysis and terrorism risk[J].Risk Analysis,2010,30(4):575-589. [4] WANG Zhen,LIU Mao.Application of quantitative risk assessment on terrorism attack[J].China Public Security,2006(4):18-22.(in Chinese) 王振,刘茂.定量风险分析在恐怖袭击风险评估中的应用[J].中国公共安全(学术版),2006(4):18-22. [5] PARNELL G S,SMITH C M,MOXLEY F I.Intelligent adversary risk analysis:a bioterrorism risk management model[J].Risk Analysis,2010,30(1):32-48. [6] COX J L A T.Some limitations of "Risk = Threat×Vulnerability×Consequence" for risk analysis of terrorist attacks[J].Risk Analysis,2008,28(6):1749-1761. [7] MERRICK J,PARNELL G S.A comparative analysis of PRA and intelligent adversary methods for counterterrorism risk management[J].Risk Analysis,2011,31(9):1488-1510. [8] WEI Jing,WANG Juyun,YU Hua.Terrorism threat assessment with multi-module Bayesian network[J].Journal of University of Chinese Academy of Sciences,2015,32(2):264-272.(in Chinese) 魏静,王菊韵,于华.基于多模块贝叶斯网络的恐怖袭击威胁评估[J].中国科学院大学学报,2015,32(2):264-272. [9] TRANCHITA C,HADJSAID N,TORRES A.Ranking contingency resulting from terrorism by utilization of Bayesian networks[C]//Proceedings of IEEE Mediter-ranean Electrotechnical Conference.Washington D.C.,USA:IEEE Press,2006:964-967. [10] POURRET O,NAIM P,MARCOT B.Terrorism risk management[M].Chichester,UK:John Wiley & Sons,2008:239-262. [11] XIANG Yin.Warning system of terrorist attacks based on improved neural network[J].Journal of Catas-trophology,2018,33(1):183-189.(in Chinese) 项寅.基于改进神经网络的恐怖袭击风险预警系统[J].灾害学,2018,33(1):183-189. [12] LI Ronggang,SUN Chunhua,JI Jianrui.Suspect characteristics prediction based on support vector machine[J].Computer Engineering,2017,43(11):198-203.(in Chinese) 李荣岗,孙春华,姬建睿.基于支持向量机的嫌疑人特征预测[J].计算机工程,2017,43(11):198-203. [13] SUN Feifei,CAO Zhuo,XIAO Xiaolei.Application of an improved random forest based classifier in crime prediction domain[J].Journal of Intelligence,2014(10):148-152.(in Chinese) 孙菲菲,曹卓,肖晓雷.基于随机森林的分类器在犯罪预测中的应用研究[J].情报杂志,2014(10):148-152. [14] ZOU B,NURUDEEN M,ZHU C,et al.A neuro-fuzzy crime prediction model based on video analysis[J].Chinese Journal of Electronics,2018,27(5):968-975. [15] LUO Senlin,LIU Zheng,GUO Liang,et al.Research on suspected culprit recognition based on probit[J].Transactions of Beijing Institute of Technology,2011(11):1337-1341.(in Chinese) 罗森林,刘峥,郭亮,等.基于Probit的犯罪嫌疑人判定方法研究[J].北京理工大学学报,2011(11):1337-1341. [16] LI Weihong,WEN Lei,CHEN Yebin.Property crime forecast based on improved GA-BP neural network model[J].Geomatics and Information Science of Wuhan University,2017,42(8):1110-1116.(in Chinese) 李卫红,闻磊,陈业滨.改进的GA-BP神经网络模型在财产犯罪预测中的应用[J].武汉大学学报(信息科学版),2017,42(8):1110-1116. [17] SIVARANJANI S,SIVAKUMARI S,AASHA M.Crime prediction and forecasting in Tamilnadu using clustering approaches[C]//Proceedings of 2016 International Conference on Emerging Technological Trends.Washington D.C.,USA:IEEE Press,2016:1-6. [18] LI Ze,SUN Duoyong,LI Bo,et al.Terrorist group behavior prediction by wavelet transform-based pattern recognition[J].Discrete Dynamics in Nature and Society,2018:1-16. [19] ZHANG Baoping.The management of foreign crime suspect under detention[J].Criminal Research,2003(5):53-58.(in Chinese) 张保平.关于恐怖主义犯罪心理和行为特点的初步研究[J].犯罪研究,2003(5):53-58. [20] JIAN Jisong.On the motives characters of the terrorism criminal[J].Journal of Hubei University of Police,2007,20(3):9-14.(in Chinese) 简基松.论恐怖主义犯罪的动机特征[J].湖北警官学院学报,2007,20(3):9-14. [21] WANG Xuemei.Analysis of the development character-istics of terrorism crime[J].Global Law Review,2013,35(1):21-33.(in Chinese) 王雪梅.恐怖主义犯罪发展特点分析[J].环球法律评论,2013,35(1):21-33. [22] ZHOU Zhihua.Machine learning[M].Beijing:Tsinghua University Press,2016.(in Chinese) 周志华.机器学习[M].北京:清华大学出版社,2016. [23] WANG Yisen,XIA Shutao.A survey of random forest algorithms[J].Information and Communications Technologies,2018,12(1):49-55.(in Chinese) 王奕森,夏树涛.集成学习之随机森林算法综述[J].信息通信技术,2018,12(1):49-55. [24] HAN Songlai,ZHANG Hui,ZHOU Huaping.A brief survey of decision tree attributes selection strategy[J].Microcomputer Applications,2007,28(8):785-790.(in Chinese) 韩松来,张辉,周华平.决策树的属性选取策略综述[J].微计算机应用,2007,28(8):785-790. [25] DING Hua,WANG Xiukun,SUN Tao.Research on decision tree method based on improved PSO[J].Mini-Micro Systems,2005,26(7):86-90.(in Chinese) 丁华,王秀坤,孙焘.基于PSO改进决策树算法的研究[J].小型微型计算机系统,2005,26(7):86-90. [26] XIE Jinmei,WANG Yanni.A survey of decision tree algorithms[J].Software Guide,2008,7(11):83-85.(in Chinese) 谢金梅,王艳妮.决策树算法综述[J].软件导刊,2008,7(11):83-85. [27] PEDREGOSA F,VAROQUAUX G,GRAMFORT A,et al.Scikit-learn:machine learning in python[J].Journal of Machine Learning Research,2011,12:2825-2830. [28] BERGSTRA J,BENGIO Y.Random search for hyper-parameter optimization[J].Journal of Machine Learning Research,2012,13:281-350. [29] BERGSTRA J,YAMINS D,COX D D.Making a science of model search:hyperparameter optimization in hundreds of dimensions for vision architectures[C]// Proceedings of the 30th International Conference on Machine Learning.Atlanta,USA:[s.n.],2013:1-9. [30] SNOEK J,LAROCHELLE H,ADAMS R P.Practical Baye-sian optimization of machine learning algorithms[C]//Proceedings of Advances in Neural Information Processing Systems.[S.l.]:Curran Associates,Inc.,2012:2951-2959. [31] BROCHU E,CORA V M,DE FREITAS N.A tutorial on Bayesian optimization of expensive cost functions,with application to active user modeling and hierarchical reinforcement learning[EB/OL].[2018-12-01].https://arxiv.xilesou.top/abs/1012.2599. [32] SHAHRIARI B,SWERSKY K,WANG Z,et al.Taking the human out of the loop:a review of Bayesian optimization[J].Proceedings of the IEEE,2016,104(1):148-175. [33] Sherpa4.10.2.[EB/OL].[2018-12-01].http://doi.org/10.5281/zenodo.2275738. |