References in zbMATH (referenced in 34 articles , 1 standard article )

Showing results 1 to 20 of 34.
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  1. Feuerriegel, Stefan; Gordon, Julius: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (2019)
  2. Victor Maus and Gilberto Câmara and Marius Appel and Edzer Pebesma: dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R (2019) not zbMATH
  3. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  4. Aggarwal, Charu C.: Machine learning for text (2018)
  5. Biecek, Przemysław: DALEX: explainers for complex predictive models in \textttR (2018)
  6. Bojan Mihaljević, Concha Bielza, Pedro Larrañaga: bnclassify: Learning Bayesian Network Classifiers (2018) not zbMATH
  7. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  8. Benjamin R. Fitzpatrick, Kerrie Mengersen: A network flow approach to visualising the roles of covariates in random forests (2017) arXiv
  9. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  10. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in (\mathbfR) (2016)
  11. Farzad Noorian and Anthony de Silva and Philip Leong: gramEvol: Grammatical Evolution in R (2016) not zbMATH
  12. Fitzpatrick, Trevor; Mues, Christophe: An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market (2016)
  13. Teisseyre, Paweł; Kłopotek, Robert A.; Mielniczuk, Jan: Random subspace method for high-dimensional regression with the \textttRpackage \textttregRSM (2016)
  14. Bertsimas, Dimitris; Brynjolfsson, Erik; Reichman, Shachar; Silberholz, John: OR forum: Tenure analytics: models for predicting research impact (2015)
  15. Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
  16. Ryan, Kenneth Joseph; Culp, Mark Vere: On semi-supervised linear regression in covariate shift problems (2015)
  17. Arratia, Argimiro: Computational finance. An introductory course with R (2014)
  18. Eugster, Manuel J. A.; Leisch, Friedrich; Strobl, Carolin: (Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms (2014)
  19. Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Amorim, Dinani: Do we need hundreds of classifiers to solve real world classification problems? (2014)
  20. Martin Sill; Thomas Hielscher; Natalia Becker; Manuela Zucknick: c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models (2014) not zbMATH

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