References in zbMATH (referenced in 138 articles )

Showing results 1 to 20 of 138.
Sorted by year (citations)

1 2 3 ... 5 6 7 next

  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Lopes, Miles E.: Estimating a sharp convergence bound for randomized ensembles (2020)
  3. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  4. Ahonen, Ilmari; Nevalainen, Jaakko; Larocque, Denis: Prediction with a flexible finite mixture-of-regressions (2019)
  5. Badih, Ghattas; Pierre, Michel; Laurent, Boyer: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
  6. 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)
  7. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  8. Cichosz, Paweł: A case study in text mining of discussion forum posts: classification with bag of words and global vectors (2019)
  9. Daniel Conn, Tuck Ngun, Gang Li, Christina M. Ramirez: Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data (2019) not zbMATH
  10. da Silva, Natalia; Alvarez-Castro, Ignacio: Clicks and cliques: exploring the soul of the community (2019)
  11. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  12. 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)
  13. 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)
  14. Gladish, Daniel W.; Darnell, Ross; Thorburn, Peter J.; Haldankar, Bhakti: Emulated multivariate global sensitivity analysis for complex computer models applied to agricultural simulators (2019)
  15. 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)
  16. Lopes, Miles E.: Estimating the algorithmic variance of randomized ensembles via the bootstrap (2019)
  17. Mercadier, Mathieu; Lardy, Jean-Pierre: Credit spread approximation and improvement using random forest regression (2019)
  18. Quach, Anna; Symanzik, Jürgen; Forsgren, Nicole: Soul of the community: an attempt to assess attachment to a community (2019)
  19. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  20. Razzaghi, Talayeh; Safro, Ilya; Ewing, Joseph; Sadrfaridpour, Ehsan; Scott, John D.: Predictive models for bariatric surgery risks with imbalanced medical datasets (2019)

1 2 3 ... 5 6 7 next