References in zbMATH (referenced in 1013 articles , 2 standard articles )

Showing results 1 to 20 of 1013.
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  1. Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi: Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification (2019)
  2. Bauer, Benedikt; Heimrich, Felix; Kohler, Michael; Krzyżak, Adam: On estimation of surrogate models for multivariate computer experiments (2019)
  3. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  4. Chen, Zhen-Yu; Fan, Zhi-Ping; Sun, Minghe: Individual-level social influence identification in social media: a learning-simulation coordinated method (2019)
  5. Feuerriegel, Stefan; Gordon, Julius: News-based forecasts of macroeconomic indicators: a semantic path model for interpretable predictions (2019)
  6. Filip, Silviu; Javeed, Aurya; Trefethen, Lloyd N.: Smooth random functions, random ODEs, and Gaussian processes (2019)
  7. Liang, Maolin; Zheng, Bing; Zhao, Ruijuan: Alternating iterative methods for solving tensor equations with applications (2019)
  8. Lopes, Miles E.: Estimating the algorithmic variance of randomized ensembles via the bootstrap (2019)
  9. Lyubchich, Vyacheslav; Woodland, Ryan J.: Using isotope composition and other node attributes to predict edges in fish trophic networks (2019)
  10. Mitra, Priyam; Lian, Heng; Mitra, Ritwik; Liang, Hua; Xie, Min-ge: A general framework for frequentist model averaging (2019)
  11. Neuberg, Richard; Glasserman, Paul: Estimating a covariance matrix for market risk management and the case of credit default swaps (2019)
  12. Powell, Warren B.: A unified framework for stochastic optimization (2019)
  13. Rossini, Jacopo; Canale, Antonio: Quantifying prediction uncertainty for functional-and-scalar to functional autoregressive models under shape constraints (2019)
  14. Yoshida, Takuma; Naito, Kanta: Regression with stagewise minimization on risk function (2019)
  15. Zhao, Qingyuan: Covariate balancing propensity score by tailored loss functions (2019)
  16. Aravkin, Aleksandr Y.; Burke, James V.; Pillonetto, Gianluigi: Generalized system identification with stable spline kernels (2018)
  17. Asfha, Huruy Debessay; Kilinc, Betul Kan: Appraisal of performance of three tree-based classification methods (2018)
  18. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  19. Ayyıldız, Ezgi; Purutçuoğlu, Vilda; Weber, Gerhard Wilhelm: Loop-based conic multivariate adaptive regression splines is a novel method for advanced construction of complex biological networks (2018)
  20. Bai, Jianchao; Zhang, Hongchao; Li, Jicheng: A parameterized proximal point algorithm for separable convex optimization (2018)

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