References in zbMATH (referenced in 87 articles , 1 standard article )

Showing results 1 to 20 of 87.
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  1. Hurley, Catherine B.; O’Connell, Mark; Domijan, Katarina: Interactive slice visualization for exploring machine learning models (2022)
  2. Pizarroso, J., Portela, J., Muñoz, A: NeuralSens: Sensitivity Analysis of Neural Networks (2022) not zbMATH
  3. Tavassoli, Abbas; Waghei, Yadollah; Nazemi, Alireza: Comparison of kriging and artificial neural network models for the prediction of spatial data (2022)
  4. Trent Henderson, Ben D. Fulcher: Feature-Based Time-Series Analysis in R using the theft Package (2022) arXiv
  5. Arellano-García, María Evarista; Camacho-Gutiérrez, José Ariel; Solorza-Calderón, Selene: Machine learning approach for higher-order interactions detection to ecological communities management (2021)
  6. Başoğlu Kabran, Fatma; Ünlü, Kamil Demirberk: A two-step machine learning approach to predict S&P 500 bubbles (2021)
  7. Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars; Bischl, Bernd: mlr3pipelines -- flexible machine learning pipelines in R (2021)
  8. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  9. Ferri-García, Ramón; Castro-Martín, Luis; del Mar Rueda, María: Evaluating machine learning methods for estimation in online surveys with superpopulation modeling (2021)
  10. Flori, Andrea; Regoli, Daniele: Revealing pairs-trading opportunities with long short-term memory networks (2021)
  11. Itsaso Rodriguez, Itziar Irigoien, Basilio Sierra, Concepcion Arenas: dbcsp: User-friendly R package for Distance-Based Common Spacial Patterns (2021) arXiv
  12. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  13. Krzysztof Gajowniczek; Tomasz Ząbkowski: ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets (2021) not zbMATH
  14. Makariou, Despoina; Barrieu, Pauline; Chen, Yining: A random forest based approach for predicting spreads in the primary catastrophe bond market (2021)
  15. Martínez-Camblor, Pablo; Pérez-Fernández, Sonia; Díaz-Coto, Susana: Optimal classification scores based on multivariate marker transformations (2021)
  16. Mistry, Miten; Letsios, Dimitrios; Krennrich, Gerhard; Lee, Robert M.; Misener, Ruth: Mixed-integer convex nonlinear optimization with gradient-boosted trees embedded (2021)
  17. Murray, Jared S.: Log-linear Bayesian additive regression trees for multinomial logistic and count regression models (2021)
  18. Nikolopoulos, Konstantinos; Punia, Sushil; Schäfers, Andreas; Tsinopoulos, Christos; Vasilakis, Chrysovalantis: Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2021)
  19. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  20. Zhang, Yumin; Sabbaghi, Arman: The designed bootstrap for causal inference in big observational data (2021)

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