References in zbMATH (referenced in 145 articles )

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  1. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  2. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Lopes, Miles E.: Estimating a sharp convergence bound for randomized ensembles (2020)
  4. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  5. Roustant, Olivier; Padonou, Espéran; Deville, Yves; Clément, Aloïs; Perrin, Guillaume; Giorla, Jean; Wynn, Henry: Group kernels for Gaussian process metamodels with categorical inputs (2020)
  6. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  7. Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
  8. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  9. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  10. Cichosz, Paweł: A case study in text mining of discussion forum posts: classification with bag of words and global vectors (2019)
  11. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  12. da Silva, Natalia; Alvarez-Castro, Ignacio: Clicks and cliques: exploring the soul of the community (2019)
  13. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  14. El Haouij, Neska; Poggi, Jean-Michel; Ghozi, Raja; Sevestre-Ghalila, Sylvie; Jaïdane, Mériem: Random forest-based approach for physiological functional variable selection for driver’s stress level classification (2019)
  15. García Nieto, P. J.; García-Gonzalo, E.; Sánchez Lasheras, F.; Paredes-Sánchez, J. P.; Riesgo Fernández, P.: Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques (2019)
  16. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  17. Gopalan, Giri; Hrafnkelsson, Birgir; Wikle, Christopher K.; Rue, Håvard; Aðalgeirsdóttir, Guðfinna; Jarosch, Alexander H.; Pálsson, Finnur: A hierarchical spatiotemporal statistical model motivated by glaciology (2019)
  18. Lopes, Miles E.: Estimating the algorithmic variance of randomized ensembles via the bootstrap (2019)
  19. Madsen, Tobias; Świtnicki, Michał; Juul, Malene; Skou Pedersen, Jakob: \textttEBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples (2019)
  20. Mercadier, Mathieu; Lardy, Jean-Pierre: Credit spread approximation and improvement using random forest regression (2019)

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