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 81 articles , 1 standard article )

Showing results 1 to 20 of 81.
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  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. Jones, Ben; Artemiou, Andreas; Li, Bing: On the predictive potential of kernel principal components (2020)
  6. 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)
  7. Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
  8. Pradeep Reddy Raamana: Kernel methods library for pattern analysis and machine learning in python (2020) arXiv
  9. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  10. Ferraro, Maria Brigida; Giordani, Paolo: A review and proposal of (fuzzy) clustering for nonlinearly separable data (2019)
  11. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  12. Haziq Jamil, Wicher Bergsma: iprior: An R Package for Regression Modelling using I-priors (2019) arXiv
  13. Jin, Shaobo; Ankargren, Sebastian: Frequentist model averaging in structural equation modelling (2019)
  14. Kumagai, Wataru; Kanamori, Takafumi: Risk bound of transfer learning using parametric feature mapping and its application to sparse coding (2019)
  15. Lasserre, Jean B.; Pauwels, Edouard: The empirical Christoffel function with applications in data analysis (2019)
  16. Quach, Anna; Symanzik, Jürgen; Forsgren, Nicole: Soul of the community: an attempt to assess attachment to a community (2019)
  17. Ramasubramanian, Karthik; Singh, Abhishek: Machine learning using R. With time series and industry-based use cases in R (2019)
  18. Yaohao, Peng; Albuquerque, Pedro Henrique Melo: Non-linear interactions and exchange rate prediction: empirical evidence using support vector regression (2019)
  19. Aggarwal, Charu C.: Machine learning for text (2018)
  20. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH

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