Kernlab

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 36 articles )

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  1. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  2. Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
  3. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  4. Bang, Sungwan; Eo, Soo-Heang; Cho, Yong Mee; Jhun, Myoungshic; Cho, HyungJun: Non-crossing weighted kernel quantile regression with right censored data (2016)
  5. 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)
  6. Sokolovska, Nataliya; Clément, Karine; Zucker, Jean-Daniel: Deep kernel dimensionality reduction for scalable data integration (2016)
  7. Cichosz, Paweł: Data mining algorithms. Explained using R (2015)
  8. 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)
  9. Härdle, Wolfgang Karl; Hlávka, Zdeněk: Multivariate statistics. Exercises and solutions (2015)
  10. Nicolas Turenne: svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery (2015) arXiv
  11. Arratia, Argimiro: Computational finance. An introductory course with R (2014)
  12. Krawczyk, Bartosz; Woźniak, Michał; Cyganek, Bogusław: Clustering-based ensembles for one-class classification (2014)
  13. Krey, Sebastian; Ligges, Uwe; Leisch, Friedrich: Music and timbre segmentation by recursive constrained $K$-means clustering (2014)
  14. Sabo, Miroslav: Consensus clustering with differential evolution (2014)
  15. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  16. Kanamori, Takafumi; Suzuki, Taiji; Sugiyama, Masashi: Computational complexity of kernel-based density-ratio estimation: a condition number analysis (2013)
  17. Kawakita, Masanori; Kanamori, Takafumi: Semi-supervised learning with density-ratio estimation (2013)
  18. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  19. Taddy, Matt: Multinomial inverse regression for text analysis (2013)
  20. Cano, Emilio L.; Moguerza, Javier M.; Redchuk, Andrés: Six Sigma with R. Statistical engineering for process improvement. (2012)

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