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Education 2.0: E-Learning Methods
In: Procedia – Social and Behavioral Sciences, Band 186, Heft 376-380
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Machine Learning Methods in Asset Pricing
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Machine Learning Methods in Asset Pricing
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Nowcasting GDP using machine learning methods
In: De Nederlandsche Bank Working Paper No. 754
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Experiments on Learning, Methods and Voting
In: Pacific economic review, Band 19, Heft 3, S. 255-259
ISSN: 1468-0106
Machine Learning Methods for Demand Estimation
In: American economic review, Band 105, Heft 5, S. 481-485
ISSN: 1944-7981
We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.
Learning Methods Preferred by Management Students
In: International journal of academic research in business and social sciences: IJ-ARBSS, Band 5, Heft 3
ISSN: 2222-6990
Indonesian Language Learning Methods in Australian Elementary Schools
In: Journal of Language and Education, Band 6(2), Heft 106-119, S. 2020
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Machine Learning Methods: Potential for Deposit Insurance
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Forecasting country conflict using statistical learning methods
In: Journal of defense analytics and logistics, Band 6, Heft 1, S. 59-72
ISSN: 2399-6439
PurposeThis paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.Design/methodology/approachIn this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.FindingsIn this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.Research limitations/implicationsThe study is based on actual historical data and is purely data driven.Practical implicationsThe study demonstrates the utility of the analytical methodology but carries not implementation recommendations.Originality/valueThis is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.
Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden
In: Österreichische Wasser- und Abfallwirtschaft, Band 73, Heft 7-8, S. 295-307
ISSN: 1613-7566
ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen explizit Speichereffekte berücksichtigt und Zeitreihen der Eingangsgrößen analysiert (entsprechend physikalisch-basierten Wasserbilanzmodellen). Dem gegenüber gestellt werden Modellierungen mit XGBoost, einer Entscheidungsbaum-Methode, die in diesem Fall nur Informationen für den zu bestimmenden Zeitschritt erhält (entsprechend physikalisch-basierten Energiebilanzmodellen). Durch diesen Vergleich der beiden Modellansätze soll untersucht werden, inwieweit sich durch die Berücksichtigung von Speichereffekten Vorteile für die Modellierung ergeben.Die Analysen zeigen, dass beide Modellansätze gute Ergebnisse erzielen und im Vergleich zu einem ausgewerteten Referenzdatensatz eine höhere Modellgüte aufweisen. Vergleicht man beide Modelle, weist das LSTM im Mittel über alle 153 untersuchten Standorte eine bessere Übereinstimmung mit den Beobachtungen auf. Allerdings zeigt sich eine Abhängigkeit der Güte der Verdunstungsvorhersage von der Vegetationsklasse des Standorts; vor allem wärmere, trockene Standorte mit kurzer Vegetation werden durch das LSTM besser repräsentiert, wohingegen beispielsweise in Feuchtgebieten XGBoost eine bessere Übereinstimmung mit den Beobachtung liefert. Die Relevanz von Speichereffekten scheint daher zwischen Ökosystemen und Standorten zu variieren.Die präsentierten Ergebnisse unterstreichen das Potenzial von Methoden der künstlichen Intelligenz für die Beschreibung der Verdunstung.
Students' perception on learning methods in engineering disciplines
This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (please insert the web address here). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited. ; [EN] Purpose - This study explores the preferences for learning methods among the students of seven engineering disciplines in a Spanish technical university. The purpose of this paper is to investigate the students' views and from them contribute to the knowledge of the effectiveness of learning methodologies. Design/methodology/approach - An online anonymous questionnaire survey was adopted to collect students' perceptions. Seven learning methods were compared in seven engineering degrees. The authors sampled 1660 students, and 426 completed responses were analysed. In addition to a descriptive analysis of the results, a multiple correspondence analysis (MCA) was performed using R data processing software. Findings - It was found that project-based learning and problem-based learning were perceived as the more effective ones. MCA identified response patterns between the preference and the efficiency of learning methods showing that students can be classified into two groups according to their preferred level of activeness in learning. Research limitations/implications - The study focusses on a single technical university and not all engineering degrees could be sampled. However, five different engineering fields were studied and no significant differences among them were found. Practical implications - The results add up to the known literature showing that students have different learning needs and consequently they perceive some methods as more effective. Instructors can use this information to strengthen their learning activities. Results also suggest that students can be classified into two groups in relation to their level of activeness in learning. This can also help to enhance general ...
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