References in zbMATH (referenced in 36 articles )

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  1. Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
  2. Cipolli, William III; Hanson, Timothy: Supervised learning via smoothed Polya trees (2019)
  3. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  4. Nengsih, Titin Agustin; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: Determining the number of components in PLS regression on incomplete data set (2019)
  5. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  6. Gilles Kratzer, Reinhard Furrer: varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets (2018) arXiv
  7. Gul, Asma; Perperoglou, Aris; Khan, Zardad; Mahmoud, Osama; Miftahuddin, Miftahuddin; Adler, Werner; Lausen, Berthold: Ensemble of a subset of (k)NN classifiers (2018)
  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. Marjolein Fokkema: pre: An R Package for Fitting Prediction Rule Ensembles (2017) arXiv
  11. Michael Hahsler and Matthew Bolaños and John Forrest: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017) not zbMATH
  12. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  13. Alquier, Pierre; Ridgway, James; Chopin, Nicolas: On the properties of variational approximations of Gibbs posteriors (2016)
  14. Tutz, Gerhard; Koch, Dominik: Improved nearest neighbor classifiers by weighting and selection of predictors (2016)
  15. Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
  16. David Conde; Miguel Fernández; Bonifacio Salvador; Cristina Rueda: dawai: An R Package for Discriminant Analysis with Additional Information (2015) not zbMATH
  17. Ishwaran, Hemant: The effect of splitting on random forests (2015)
  18. Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot: VSURF: An R Package for Variable Selection Using Random Forests (2015) not zbMATH
  19. Chen, Yu-Chuan; Ha, Hyejung; Kim, Hyunjoong; Ahn, Hongshik: Canonical forest (2014)
  20. Hino, Hideitsu; Murata, Noboru: A nonparametric clustering algorithm with a quantile-based likelihood estimator (2014)

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