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 95 articles , 1 standard article )

Showing results 1 to 20 of 95.
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  1. Batool, Fatima; Hennig, Christian: Clustering with the average silhouette width (2021)
  2. Fitzpatrick, Trevor; Mues, Christophe: How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (2021)
  3. Jared D. Huling, Menggang Yu: Subgroup Identification Using the personalized Package (2021) not zbMATH
  4. Kolosova, Tanya; Berestizhevsky, Samuel: Supervised machine learning. Optimization framework and applications with SAS and R (2021)
  5. Van Belle, Jente; Guns, Tias; Verbeke, Wouter: Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (2021)
  6. 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
  7. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  8. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  9. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  10. Bubenik, Peter; Hull, Michael; Patel, Dhruv; Whittle, Benjamin: Persistent homology detects curvature (2020)
  11. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  12. Jones, Ben; Artemiou, Andreas; Li, Bing: On the predictive potential of kernel principal components (2020)
  13. 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)
  14. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  15. Kumar, Sandeep; Ying, Jiaxi; Cardoso, José Vinícius de M.; Palomar, Daniel P.: A unified framework for structured graph learning via spectral constraints (2020)
  16. Larkin, Taylor; Mcmanus, Denise: An analytical toast to wine: using stacked generalization to predict wine preference (2020)
  17. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH
  18. Pradeep Reddy Raamana: Kernel methods library for pattern analysis and machine learning in python (2020) arXiv
  19. Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Jeff Wu, C. F.: A generalized Gaussian process model for computer experiments with binary time series (2020)
  20. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)

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