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 88 articles , 1 standard article )

Showing results 1 to 20 of 88.
Sorted by year (citations)

1 2 3 4 5 next

  1. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  2. Boehmke, Brad; Greenwell, Brandon M.: Hands-on machine learning with R (2020)
  3. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  4. Bubenik, Peter; Hull, Michael; Patel, Dhruv; Whittle, Benjamin: Persistent homology detects curvature (2020)
  5. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  6. Jones, Ben; Artemiou, Andreas; Li, Bing: On the predictive potential of kernel principal components (2020)
  7. 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)
  8. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  9. Kumar, Sandeep; Ying, Jiaxi; Cardoso, José Vinícius de M.; Palomar, Daniel P.: A unified framework for structured graph learning via spectral constraints (2020)
  10. Larkin, Taylor; Mcmanus, Denise: An analytical toast to wine: using stacked generalization to predict wine preference (2020)
  11. 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
  12. Pradeep Reddy Raamana: Kernel methods library for pattern analysis and machine learning in python (2020) arXiv
  13. 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)
  14. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  15. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  16. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  17. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  18. Jin, Shaobo; Ankargren, Sebastian: Frequentist model averaging in structural equation modelling (2019)
  19. Kumagai, Wataru; Kanamori, Takafumi: Risk bound of transfer learning using parametric feature mapping and its application to sparse coding (2019)
  20. Lasserre, Jean B.; Pauwels, Edouard: The empirical Christoffel function with applications in data analysis (2019)

1 2 3 4 5 next