References in zbMATH (referenced in 1104 articles )

Showing results 1 to 20 of 1104.
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  1. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  2. Chelly Dagdia, Zaineb; Elouedi, Zied: A hybrid fuzzy maintained classification method based on dendritic cells (2020)
  3. De Bock, Koen W.; Coussement, Kristof; Lessmann, Stefan: Cost-sensitive business failure prediction when misclassification costs are uncertain: a heterogeneous ensemble selection approach (2020)
  4. Fujita, Hamido; Ko, Yu-Chien: A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF (2020)
  5. Jacobs, Kayla; Itai, Alon; Wintner, Shuly: Acronyms: identification, expansion and disambiguation (2020)
  6. Khan, Zardad; Gul, Asma; Perperoglou, Aris; Miftahuddin, Miftahuddin; Mahmoud, Osama; Adler, Werner; Lausen, Berthold: Ensemble of optimal trees, random forest and random projection ensemble classification (2020)
  7. Mohanty, Monalisa; Biswal, Pradyut; Sabut, Sukanta: Machine learning approach to recognize ventricular arrhythmias using VMD based features (2020)
  8. Nguyen, Thi Thanh Sang; Do, Pham Minh Thu: Classification optimization for training a large dataset with naïve Bayes (2020)
  9. Ruehle, Fabian: Data science applications to string theory (2020)
  10. Wang, Jie; Wang, Bo; Liang, Jing; Yu, Kunjie; Yue, Caitong; Ren, Xiangyang: Ensemble learning via multimodal multiobjective differential evolution and feature selection (2020)
  11. Yang, Shiueng-Bien; Chen, Tai-Liang: Uncertain decision tree for bank marketing classification (2020)
  12. Armengol, Eva; Boixader, Dionís; García-Cerdaña, Àngel; Recasens, Jordi: (T)-generable indistinguishability operators and their use for feature selection and classification (2019)
  13. Boullé, Marc; Charnay, Clément; Lachiche, Nicolas: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data (2019)
  14. Bruni, Renato; Bianchi, Gianpiero; Dolente, Cosimo; Leporelli, Claudio: Logical analysis of data as a tool for the analysis of probabilistic discrete choice behavior (2019)
  15. 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)
  16. Dvořák, Jakub: Classification trees with soft splits optimized for ranking (2019)
  17. Karaca, Yeliz; Cattani, Carlo: Computational methods for data analysis (2019)
  18. Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panagiotis: A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays (2019)
  19. Patil, Abhijeet R.; Chang, Jongwha; Leung, Ming-Ying; Kim, Sangjin: Analyzing high dimensional correlated data using feature ranking and classifiers (2019)
  20. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)

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