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A data-driven software tool for enabling cooperative information sharing among police departments

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  • Redmond, Michael
  • Baveja, Alok

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  • Redmond, Michael & Baveja, Alok, 2002. "A data-driven software tool for enabling cooperative information sharing among police departments," European Journal of Operational Research, Elsevier, vol. 141(3), pages 660-678, September.
    • Handle: RePEc:eee:ejores:v:141:y:2002:i:3:p:660-678
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    References listed on IDEAS

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    1. Uma G. Gupta, 1994. "How Case-Based Reasoning Solves New Problems," Interfaces, INFORMS, vol. 24(6), pages 110-119, December.
    2. Baveja, A. & Feichtinger, G. & Hartl, R.F. & Haunschmied, J.L. & Kort, P.M., 1999. "A Resource-Constrained Optimal Control Model for Crackdown on Illicit Drug Markets," Other publications TiSEM 8648e4a1-5cc5-4167-8aad-9, Tilburg University, School of Economics and Management.
    3. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    4. Naik, Ashich V. & Baveja, Alok & Batta, Rajan & Caulkins, Jonathan P., 1996. "Scheduling crackdowns on illicit drug markets," European Journal of Operational Research, Elsevier, vol. 88(2), pages 231-250, January.
    5. Baveja, Alok & Caulkins, Jonathan P. & Liu, Wensheng & Batta, Rajan & Karwan, Mark H., 1997. "When haste makes sense: Cracking down on street markets for illicit drugs," Socio-Economic Planning Sciences, Elsevier, vol. 31(4), pages 293-306, December.
    6. Northrop, Alana & Kraemer, Kenneth L. & King, John Leslie, 1995. "Police use of computers," Journal of Criminal Justice, Elsevier, vol. 23(3), pages 259-275.
    7. Baveja, Alok & Batta, Rajan & Caulkins, Jonathan P. & Karwan, Mark H., 1993. "Modeling the response of illicit drug markets to local enforcement," Socio-Economic Planning Sciences, Elsevier, vol. 27(2), pages 73-89, June.
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    Cited by:

    1. Gérard Biau & Erwan Scornet & Johannes Welbl, 2019. "Neural Random Forests," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 347-386, December.
    2. van Erp, Sara & Oberski, Daniel L. & Mulder, Joris, 2018. "Shrinkage priors for Bayesian penalized regression," OSF Preprints cg8fq, Center for Open Science.
    3. Fosso Wamba, Samuel & Bawack, Ransome Epie & Guthrie, Cameron & Queiroz, Maciel M. & Carillo, Kevin Daniel André, 2021. "Are we preparing for a good AI society? A bibliometric review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    4. Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo & M. Remedios Sillero-Denamiel, 2021. "A cost-sensitive constrained Lasso," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 121-158, March.
    5. Dahlgren, Joy & Khattak, Asad & McDonough, Patrick & Banerjee, Ipsita & Orrick, Phyllis & Sharafsaleh, Ashkan, 2004. "ITS Decision Enhancements: Developing Case-Based Reasoning and Expert Systems and Incorporating New Material," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9242q08h, Institute of Transportation Studies, UC Berkeley.
    6. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    7. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.

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