Machine learning, artificial neural networks and social research
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DOI: 10.1007/s11135-020-01037-y
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References listed on IDEAS
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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Cited by:
- Mohamed-Amine Babay & Mustapha Adar & Ahmed Chebak & Mustapha Mabrouki, 2023. "Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning," Energies, MDPI, vol. 16(14), pages 1-21, July.
- Hüseyin Cüce & Duygu Özçelik, 2022. "Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
- Wadim Strielkowski & Svetlana Zenchenko & Anna Tarasova & Yana Radyukova, 2022. "Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
- Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.
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Keywords
Machine learning; Deep learning Artificial neural network; Supervised learning; Linear models; Nonlinear models;All these keywords.
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