References in zbMATH (referenced in 111 articles )

Showing results 1 to 20 of 111.
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  1. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  2. Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
  3. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  4. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  5. Lopes, Miles E.: Estimating the algorithmic variance of randomized ensembles via the bootstrap (2019)
  6. Mercadier, Mathieu; Lardy, Jean-Pierre: Credit spread approximation and improvement using random forest regression (2019)
  7. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  8. Steingrimsson, Jon Arni; Diao, Liqun; Strawderman, Robert L.: Censoring unbiased regression trees and ensembles (2019)
  9. Wu, Yan-Xue; Min, Xue-Yang; Min, Fan; Wang, Min: Cost-sensitive active learning with a label uniform distribution model (2019)
  10. Aggarwal, Charu C.: Machine learning for text (2018)
  11. Alicja Gosiewska; Przemyslaw Biecek: auditor: an R Package for Model-Agnostic Visual Validation and Diagnostic (2018) arXiv
  12. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  13. Bogaert, Matthias; Ballings, Michel; Van den Poel, Dirk: Evaluating the importance of different communication types in romantic tie prediction on social media (2018)
  14. Champion, Magali; Picheny, Victor; Vignes, Matthieu: Inferring large graphs using (\ell_1)-penalized likelihood (2018)
  15. Hernández, Belinda; Raftery, Adrian E.; Pennington, Stephen R.; Parnell, Andrew C.: Bayesian additive regression trees using Bayesian model averaging (2018)
  16. Hooker, Giles; Mentch, Lucas: Bootstrap bias corrections for ensemble methods (2018)
  17. Jafrasteh, Bahram; Fathianpour, Nader; Suárez, Alberto: Comparison of machine learning methods for copper ore grade estimation (2018)
  18. Janitza, Silke; Celik, Ender; Boulesteix, Anne-Laure: A computationally fast variable importance test for random forests for high-dimensional data (2018)
  19. Lukas W. Lehnert, Hanna Meyer, Wolfgang A. Obermeier, Brenner Silva, Bianca Regeling, Jörg Bendix: Hyperspectral Data Analysis in R: the hsdar Package (2018) arXiv
  20. Probst, Philipp; Boulesteix, Anne-Laure: To tune or not to tune the number of trees in random forest (2018)

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