Search Results - subject:"Learning methods"

The post COVID-19 pandemic era : changes in teaching and learning methods for management educators

Ratten, Vanessa - In: The international journal of management education 21 (2023) 2, pp. 1-5

Persistent link: https://www.econbiz.de/10014327081

A hybrid approach for predicting missing follower–followee links in social networks using topological features with ensemble learning

Bhattacharya, Riju; Nagwani, Naresh Kumar; Tripathi, Sarsij - In: Data Technologies and Applications 57 (2023) 1, pp. 131-153

hybrid features on data sets. This was followed by using benchmark classifiers and ensemble learning methods. The experiments …

Persistent link: https://www.econbiz.de/10014712628

Completing the market : generating shadow CDS spreads by machine learning

Hu, Nan; Li, Jian; Meyer Cirkel, Alexis - 2019

ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and …-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add …

Persistent link: https://www.econbiz.de/10012155143

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Achterberg, Massimo A.; Prasse, Bastian; Ma, Long; … - In: International journal of forecasting 38 (2022) 2, pp. 489-504

Persistent link: https://www.econbiz.de/10013348612

International marketing curriculum and instructional pedagogies : cross-national differences in business students

Spillan, John E.; Kara, Ali - In: Journal of teaching in international business 33 (2022) 2/3, pp. 149-171

Persistent link: https://www.econbiz.de/10013417133

Using data mining techniques for detecting the important features of the bank direct marketing data

Parlar, Tuba; Kakilli Acaravci, Songul - In: International journal of economics and financial issues … 7 (2017) 2, pp. 692-696

Persistent link: https://www.econbiz.de/10011809107

Chasing Balance and Other Recommendations for Improving Nonparametric Propensity Score Models

Griffin, Beth Ann; McCaffrey, Daniel F.; Almirall, Daniel; … - In: Journal of Causal Inference 5 (2017) 2

Abstract: In this article, we carefully examine two important implementation issues when estimating propensity scores using generalized boosted models (GBM), a promising machine learning technique. First, we examine which of the following methods for tuning GBM lead to better covariate balance...

Persistent link: https://www.econbiz.de/10014610835

Kaggle forecasting competitions : an overlooked learning opportunity

Bojer, Casper Solheim; Meldgaard, Jens Peder - In: International journal of forecasting 37 (2021) 2, pp. 587-603

Persistent link: https://www.econbiz.de/10012792855

Impact of students' cultural values on the corporate entrepreneurship management linked to social responsibility

Escobar-Sierra, Manuela; Calderón-Valencia, Felipe - In: International journal of intellectual property … 10 (2020) 4, pp. 375-395

Persistent link: https://www.econbiz.de/10012511833

The M4 competition : 100,000 time series and 61 forecasting methods

Makridakis, Spyros G.; Spiliotis, Evangelos; … - In: International journal of forecasting 36 (2020) 1, pp. 54-74

Persistent link: https://www.econbiz.de/10012405558