References in zbMATH (referenced in 1162 articles )

Showing results 1 to 20 of 1162.
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  1. Gao, Kun; Wang, Hanpin; Cao, Yongzhi; Inoue, Katsumi: Learning from interpretation transition using differentiable logic programming semantics (2022)
  2. Georgiev, Slavi G.; Vulkov, Lubin G.: Recovering the time-dependent volatility in jump-diffusion models from nonlocal price observations (2022)
  3. Kim, Ahhyoun; Kim, Hyunjoong: A new classification tree method with interaction detection capability (2022)
  4. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  5. Adams, Ben; Walter, Katharine S.; Diuk-Wasser, Maria A.: Host specialisation, immune cross-reaction and the composition of communities of co-circulating \textitBorreliastrains (2021)
  6. Ahn, Gilseung; Park, You-Jin; Hur, Sun: A membership probability-based undersampling algorithm for imbalanced data (2021)
  7. Artiemjew, Piotr; Ropiak, Krzysztof: A novel ensemble model -- the random granular reflections (2021)
  8. Ballante, Elena; Galvani, Marta; Uberti, Pierpaolo; Figini, Silvia: Polarized classification tree models: theory and computational aspects (2021)
  9. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  10. Bruto da Costa, Antonio Anastasio; Dasgupta, Pallab: Learning temporal causal sequence relationships from real-time time-series (2021)
  11. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  12. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  13. Gan, Guojun; Ma, Chaoqun; Wu, Jianhong: Data clustering. Theory, algorithms, and applications (2021)
  14. Ghods, Alireza; Cook, Diane J.: A survey of deep network techniques all classifiers can adopt (2021)
  15. Günlük, Oktay; Kalagnanam, Jayant; Li, Minhan; Menickelly, Matt; Scheinberg, Katya: Optimal decision trees for categorical data via integer programming (2021)
  16. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  17. Krzysztof Gajowniczek; Tomasz Ząbkowski: ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets (2021) not zbMATH
  18. Malan, Katherine M.: Online landscape analysis for guiding constraint handling in particle swarm optimisation (2021)
  19. Maliah, Shlomi; Shani, Guy: Using POMDPs for learning cost sensitive decision trees (2021)
  20. Margot, Vincent; Baudry, Jean-Patrick; Guilloux, Frederic; Wintenberger, Olivier: Consistent regression using data-dependent coverings (2021)

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