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

Keywords for this software

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References in zbMATH (referenced in 109 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. Gopalan, Giri; Wikle, Christopher K.: A higher-order singular value decomposition tensor emulator for spatiotemporal simulators (2022)
  5. Hennig, Christian: An empirical comparison and characterisation of nine popular clustering methods (2022)
  6. Howard, Emma; Cronin, Anthony: Improving service use through prediction modelling: a case study of a mathematics support centre (2022)
  7. Mohammed Rashid, Abdullah; Midi, Habshah; Dhhan, Waleed; Arasan, Jayanthi: Detection of outliers in high-dimensional data using \textitnu-support vector regression (2022)
  8. Yu, Yanjia; Yang, Yi; Yang, Yuhong: Performance assessment of high-dimensional variable identification (2022)
  9. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  10. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  11. Jared D. Huling, Menggang Yu: Subgroup Identification Using the personalized Package (2021) not zbMATH
  12. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  13. Park, Heewon; Konishi, Sadanori: Sparse kernel subspace method for classifying and representing patterns from data with complex structure (2021)
  14. Travis-Lumer, Yael; Goldberg, Yair: Kernel machines for current status data (2021)
  15. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  16. 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
  17. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  18. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  19. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  20. Bubenik, Peter; Hull, Michael; Patel, Dhruv; Whittle, Benjamin: Persistent homology detects curvature (2020)

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