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Härdle, Wolfgang Voß, Stefan Johnson, Johnnie E. V. Seow, Hsin-Vonn De Bock, Koen W. Zharova, Alona Coussement, Kristof Haupt, Johannes Sebastian Sung, Ming-Chien Stahlbock, Robert Sung, M.-C. Ma, Tiejun Jaroszewicz, Szymon Gubela, Robin M. Dautel, Alexander Jakob Guhl, Daniel Kozodoi, Nikita Caigny, Arno de Baesens, Bart Verbeke, Wouter Fabian, Benjamin Gebert, Fabian Bokelmann, Björn Behl, Abhishek Dutta, Pankaj Dwivedi, Yogesh K. Kar, Samarjit Bender, Benedict Dress, Korbinian Schüller, Sebastian Mettenheim, Hans-Jörg von Haupt, Johannes Thomas, Lyn C. Abou-Nasr, Mahmoud Słowiński, Roman Weiss, Gary M. Preßmar, Dieter B. Klein, Nadja Petersen, Wiebke Maldonado, Sebastián Weber, Richard Vairetti, Carla Gabel, Sebastian Klapper, Daniel Óskarsdóttir, María Martens, David Jacob, Johannes Lux, Marius Gubela, Robin Marco Kim, A. Yang, Y. Delen, Dursun Benoit, D. F. All co-authors data research prognoseverfahren learning uplift models using support model paper machine deep based mining sciences performance revenue approach management neural decomposition modeling prediction universities structure tpe relationship exchange recurrent networks response garch financial level campaign framework making case rate targeting value svr kde behavior customer network evaluation forecast vector teaching outputs test faculties social humanities number causal var prognose beziehungsmarketing konsumentenverhalten applications credit policy university study forecasting scientific marketing transformation implications customers hybrid selection return information events economy functions
Composed data mining forecasting model künstliche intelligenz artificial intelligence big data machine learning revenue uplift uplift modeling deep learning rate forecasting forecasting using neural networks support vector management universities research outputs social sciences sciences humanities tpe number neuronale netze relationship marketing consumer behaviour credit risk policy decision research management case study exchange rate recurrent neural svr garch garch kde kde hybrid neural network real world vector machines paper proposes uplift models risk management decision analysis credit rating time series analysis data protection e commerce data driven forex exchange using deep deep recurrent scientific performance performance function function funds response transformation transformation profit profit decomposition decomposition revenue credit scoring value risk data analytics return marketing world data mining support support vektor vektor maschine universities demands demands data data teaching teaching research performance while while teaching teaching parameters parameters measured measured student student performance performance teacher teacher evaluation evaluation programs programs connection connection research outputs grant antecedents harder harder check check test test understand understand paper paper elicits elicits interdependence structure party party expenses expenses tpe tpe publications level data data sample period using
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The information on the author is retrieved from: Entity Facts (by DNB = German National Library data service), DBPedia and Wikidata
Stefan Lessmann Prof. Dr. Alternative spellings: S. Lessmann B: 1975
Profession Kaufmann
Affiliations Universität. Wirtschaftswissenschaftliche Fakultät (Humboldt-Universität) Universität Hamburg. Fakultät Wirtschafts- und Sozialwissenschaften
Q102680475
Publishing years Series SFB 649 Discussion Paper (1) IRTG 1792 Discussion Paper 2018-001 (1) SFB 649 discussion paper (1) Annals of Information Systems (1) 
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