Kernlab

R package kernlab: Kernel-based Machine Learning Lab. Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver


References in zbMATH (referenced in 104 articles , 1 standard article )

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  1. Abolghasemi, Mahdi; Hyndman, Rob J.; Spiliotis, Evangelos; Bergmeir, Christoph: Model selection in reconciling hierarchical time series (2022)
  2. Anderlucci, Laura; Fortunato, Francesca; Montanari, Angela: High-dimensional clustering via random projections (2022)
  3. Etienne Côme, Nicolas Jouvin : greed: An R Package for Model-Based Clustering by Greedy Maximization of the Integrated Classification Likelihood (2022) arXiv
  4. Yu, Yanjia; Yang, Yi; Yang, Yuhong: Performance assessment of high-dimensional variable identification (2022)
  5. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  6. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  7. Jared D. Huling, Menggang Yu: Subgroup Identification Using the personalized Package (2021) not zbMATH
  8. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  9. Travis-Lumer, Yael; Goldberg, Yair: Kernel machines for current status data (2021)
  10. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  11. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  12. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  13. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  14. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  15. Bubenik, Peter; Hull, Michael; Patel, Dhruv; Whittle, Benjamin: Persistent homology detects curvature (2020)
  16. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  17. Hwang, Heungsun; Cho, Gyeongcheol: Global least squares path modeling: a full-information alternative to partial least squares path modeling (2020)
  18. Jones, Ben; Artemiou, Andreas; Li, Bing: On the predictive potential of kernel principal components (2020)
  19. 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)
  20. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)

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