References in zbMATH (referenced in 62 articles , 1 standard article )

Showing results 1 to 20 of 62.
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  1. Anthony D. Blaom, Franz Kiraly, Thibaut Lienart, Yiannis Simillides, Diego Arenas, Sebastian J. Vollmer: MLJ: A Julia package for composable Machine Learning (2020) arXiv
  2. Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
  3. Chambaz, Antoine; Benkeser, David: A ride in targeted learning territory (2020)
  4. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  5. Gero Szepannek: An Overview on the Landscape of R Packages for Credit Scoring (2020) arXiv
  6. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  7. Larkin, Taylor; Mcmanus, Denise: An analytical toast to wine: using stacked generalization to predict wine preference (2020)
  8. Neeraj Dhanraj Bokde; Gorm Bruun Andersen: ForecastTB - An R Package as a Test-bench for Forecasting Methods Comparison (2020) arXiv
  9. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  10. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  11. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  12. Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek: Landscape of R packages for eXplainable Artificial Intelligence (2020) arXiv
  13. Tomčala, Jiří: Predictability and entropy of supercomputer infrastructure consumption (2020)
  14. Yu, Weichang; Ormerod, John T.; Stewart, Michael: Variational discriminant analysis with variable selection (2020)
  15. Ahsen, Mehmet Eren; Vogel, Robert M.; Stolovitzky, Gustavo A.: Unsupervised evaluation and weighted aggregation of ranked classification predictions (2019)
  16. Azmi, Mohamed; Runger, George C.; Berrado, Abdelaziz: Interpretable regularized class association rules algorithm for classification in a categorical data space (2019)
  17. Baíllo, Amparo; Cárcamo, Javier; Getman, Konstantin: New distance measures for classifying X-ray astronomy data into stellar classes (2019)
  18. Berrendero, José R.; Cárcamo, Javier: Linear components of quadratic classifiers (2019)
  19. Carpita, Maurizio; Ciavolino, Enrico; Pasca, Paola: Exploring and modelling team performances of the kaggle European soccer database (2019)
  20. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv

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