References in zbMATH (referenced in 1202 articles , 2 standard articles )

Showing results 21 to 40 of 1202.
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  1. Moreno, Sebastián; Pereira, Jordi; Yushimito, Wilfredo: A hybrid K-means and integer programming method for commercial territory design: a case study in meat distribution (2020)
  2. Rabinowicz, Assaf; Rosset, Saharon: Assessing prediction error at interpolation and extrapolation points (2020)
  3. Schnaubelt, Matthias; Fischer, Thomas G.; Krauss, Christopher: Separating the signal from the noise -- financial machine learning for Twitter (2020)
  4. Sheng, Baohuai; Liu, Huanxiang; Wang, Huimin: Learning rates for the kernel regularized regression with a differentiable strongly convex loss (2020)
  5. Voelkel, Michael A.; Sachs, Anna-Lena; Thonemann, Ulrich W.: An aggregation-based approximate dynamic programming approach for the periodic review model with random yield (2020)
  6. Winkler, Joab R.; Mitrouli, Marilena: Condition estimation for regression and feature selection (2020)
  7. Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi: Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification (2019)
  8. Adler, Robert J.; Agami, Sarit: Modelling persistence diagrams with planar point processes, and revealing topology with bagplots (2019)
  9. Afendras, Georgios; Markatou, Marianthi: Optimality of training/test size and resampling effectiveness in cross-validation (2019)
  10. Aït-Sahalia, Yacine; Xiu, Dacheng: Principal component analysis of high-frequency data (2019)
  11. Angelelli, Mario: Complexity reduction for sign configurations through the KP II equation and its information-theoretic aspects (2019)
  12. Arakelian, Veni; Dellaportas, Petros; Savona, Roberto; Vezzoli, Marika: Sovereign risk zones in Europe during and after the debt crisis (2019)
  13. Arashi, M.; Roozbeh, Mahdi: Some improved estimation strategies in high-dimensional semiparametric regression models with application to riboflavin production data (2019)
  14. Aravkin, Aleksandr Y.; Bottegal, Giulio; Pillonetto, Gianluigi: Boosting as a kernel-based method (2019)
  15. Azencott, Robert; Muravina, Viktoria; Hekmati, Rasoul; Zhang, Wei; Paldino, Michael: Automatic clustering in large sets of time series (2019)
  16. Bacinello, Anna Rita; Zoccolan, Ivan: Variable annuities with a threshold fee: valuation, numerical implementation and comparative static analysis (2019)
  17. Barrera, David; Gobet, Emmanuel: Quantitative bounds for concentration-of-measure inequalities and empirical regression: the independent case (2019)
  18. Bauer, Benedikt; Heimrich, Felix; Kohler, Michael; Krzyżak, Adam: On estimation of surrogate models for multivariate computer experiments (2019)
  19. Bax, Eric; Weng, Lingjie; Tian, Xu: Speculate-correct error bounds for (k)-nearest neighbor classifiers (2019)
  20. Bhadra, Anindya; Datta, Jyotishka; Li, Yunfan; Polson, Nicholas G.; Willard, Brandon: Prediction risk for the horseshoe regression (2019)

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