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 48 articles )

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  1. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018)
  2. Muñoz, Mario A.; Villanova, Laura; Baatar, Davaatseren; Smith-Miles, Kate: Instance spaces for machine learning classification (2018)
  3. Wang, Boxiang; Zou, Hui: Another look at distance-weighted discrimination (2018)
  4. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  5. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  6. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  7. 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)
  8. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  9. Bang, Sungwan; Eo, Soo-Heang; Cho, Yong Mee; Jhun, Myoungshic; Cho, HyungJun: Non-crossing weighted kernel quantile regression with right censored data (2016)
  10. 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)
  11. Sokolovska, Nataliya; Clément, Karine; Zucker, Jean-Daniel: Deep kernel dimensionality reduction for scalable data integration (2016)
  12. Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
  13. 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)
  14. Härdle, Wolfgang Karl; Hlávka, Zdeněk: Multivariate statistics. Exercises and solutions (2015)
  15. Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015)
  16. Nicolas Turenne: svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery (2015) arXiv
  17. Arratia, Argimiro: Computational finance. An introductory course with R (2014)
  18. Krawczyk, Bartosz; Woźniak, Michał; Cyganek, Bogusław: Clustering-based ensembles for one-class classification (2014)
  19. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained $K$-means clustering (2014)
  20. Roger Koenker and Ivan Mizera: Convex Optimization in R (2014)

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