e1071

R package e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...


References in zbMATH (referenced in 98 articles )

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  1. Ertefaie, Ashkan; McKay, James R.; Oslin, David; Strawderman, Robert L.: Robust Q-learning (2021)
  2. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  3. Blanco, Víctor; Japón, Alberto; Puerto, Justo: Optimal arrangements of hyperplanes for SVM-based multiclass classification (2020)
  4. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  5. Cai, Zhanrui; Li, Runze; Zhu, Liping: Online sufficient dimension reduction through sliced inverse regression (2020)
  6. Elman, Miriam R.; Minnier, Jessica; Chang, Xiaohui; Choi, Dongseok: Noise accumulation in high dimensional classification and total signal index (2020)
  7. Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
  8. 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)
  9. Lai, Yuanhao; McLeod, Ian: Ensemble quantile classifier (2020)
  10. Pan, Yuqing; Mai, Qing: Efficient computation for differential network analysis with applications to quadratic discriminant analysis (2020)
  11. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)
  12. Sheng, Ying; Wang, Qihua: Model-free feature screening for ultrahigh dimensional classification (2020)
  13. Bagirov, Adil; Taheri, Sona; Asadi, Soodabeh: A difference of convex optimization algorithm for piecewise linear regression (2019)
  14. Bak, Kwan-Young; Jhong, Jae-Hwan; Koo, Ja-Yong: Spatially adaptive binary classifier using B-splines and total variation penalty (2019)
  15. Christoph Mssel, Ludwig Lausser, Markus Maucher, Hans A. Kestler: Multi-Objective Parameter Selection for Classifiers (2019) not zbMATH
  16. Cichosz, Paweł: A case study in text mining of discussion forum posts: classification with bag of words and global vectors (2019)
  17. Cipolli, William III; Hanson, Timothy: Supervised learning via smoothed Polya trees (2019)
  18. Davis, Benjamin J. K.; Curriero, Frank C.: Development and evaluation of geostatistical methods for non-Euclidean-based spatial covariance matrices (2019)
  19. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  20. Ge, Li; Liu, Jiaguo; Zhang, Yusen; Dehmer, Matthias: Identifying anticancer peptides by using a generalized chaos game representation (2019)

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