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 57 articles , 1 standard article )

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  1. Aggarwal, Charu C.: Machine learning for text (2018)
  2. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  3. Gul, Asma; Perperoglou, Aris; Khan, Zardad; Mahmoud, Osama; Miftahuddin, Miftahuddin; Adler, Werner; Lausen, Berthold: Ensemble of a subset of $k$NN classifiers (2018)
  4. Jin Zhu, Wenliang Pan, Wei Zheng, Xueqin Wang: Ball: An R package for detecting distribution difference and association in metric spaces (2018) arXiv
  5. Mair, Patrick: Modern psychometrics with R (2018)
  6. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  7. Wang, Boxiang; Zou, Hui: Another look at distance-weighted discrimination (2018)
  8. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  9. Bommert, Andrea; Rahnenführer, Jörg; Lang, Michel: A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data (2017)
  10. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  11. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  12. Islam, Shofiqul; Anand, Sonia; Hamid, Jemila; Thabane, Lehana; Beyene, Joseph: Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration (2017)
  13. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  14. Bang, Sungwan; Eo, Soo-Heang; Cho, Yong Mee; Jhun, Myoungshic; Cho, HyungJun: Non-crossing weighted kernel quantile regression with right censored data (2016)
  15. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  16. Sokolovska, Nataliya; Clément, Karine; Zucker, Jean-Daniel: Deep kernel dimensionality reduction for scalable data integration (2016)
  17. Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
  18. Fernandez-Lozano, Carlos; Cuiñas, Rubén F.; Seoane, José A.; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R.: Classification of signaling proteins based on molecular star graph descriptors using machine learning models (2015)
  19. Härdle, Wolfgang Karl; Hlávka, Zdeněk: Multivariate statistics. Exercises and solutions (2015)
  20. Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015) not zbMATH

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