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.


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Reference manual

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install.packages("kernlab")

0.9-29 by Alexandros Karatzoglou, 6 days ago


Browse source code at https://github.com/cran/kernlab


Authors: Alexandros Karatzoglou [aut, cre] , Alex Smola [aut] , Kurt Hornik [aut] , National ICT Australia (NICTA) [cph] , Michael A. Maniscalco [ctb, cph] , Choon Hui Teo [ctb]


Documentation:   PDF Manual  


Task views: Cluster Analysis & Finite Mixture Models, Machine Learning & Statistical Learning, Multivariate Statistics, Natural Language Processing, Optimization and Mathematical Programming


GPL-2 license


Imports methods, stats, grDevices, graphics

System requirements: C++11


Imported by ABPS, BKPC, CondIndTests, DeLorean, DynTxRegime, GeneralisedCovarianceMeasure, ITRLearn, KRMM, MTGS, MachineShop, PCDimension, RMKL, ROI.plugin.ipop, RSSL, STGS, SVMMaj, SwarmSVM, Synth, VRPM, brainKCCA, calibrateBinary, diceR, fPortfolio, fpc, funcy, gkmSVM, kernelFactory, kernelPSI, kpcalg, ks, microsynth, nlcv, personalized, plsRcox, qrjoint, qrsvm, rminer, robCompositions, rres, soilassessment, survivalsvm, tsensembler, tsiR.

Depended on by CVST, DRR, DTRlearn2, RaPKod, exprso, kappalab, kfda, probsvm, svmadmm.

Suggested by BiodiversityR, CompareCausalNetworks, FactorsR, GAparsimony, MSCMT, RStoolbox, Semblance, SuperLearner, bWGR, breakDown, butcher, caret, caretEnsemble, colorspace, condvis2, dials, dimRed, dismo, evtree, fscaret, gamclass, kernelTDA, loon, mistral, mlr, mlr3pipelines, mlrMBO, parsnip, pdp, pmml, preprocomb, rattle, recipes, sand, shipunov, spectralGraphTopology, ssc, supervisedPRIM, vcd.

Enhanced by clue, prediction.


See at CRAN