kernlab - An S4 Package for Kernel Methods in R

By Alexandros Karatzoglou, A. Smola, Kurt Hornik, Achim Zeileis. 2004

2,010 citations recorded by the source corpus.

kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R's new S4 ob ject model and provides a framework for creating and using kernel-based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference (2015, methodology): “We use a spectrum kernel (also called a string kernel, the default in [Karatzoglou et al. 2004]) which matches substrings of length m = 3.”
pdp: An R Package for Constructing Partial Dependence Plots (2017, neutral): “…Bagged decision trees adabag (Alfaro et al., 2013) "bagging" ipred (Peters and Hothorn, 2017) "classbagg" , "regbagg" (Wright, 2017) "ranger" Support vector machine e1071 (Meyer et al., 2017) "svm" kernlab (Karatzoglou et al., 2004) "ksvm" Table 1: Models speciﬁcally supported by the pdp package.”
The stringdist Package for Approximate String Matching (2014, neutral): “Finally, it is worth mentioning that these families of string metrics have recently been extended with metrics based on string kernels (Lodhi et al., 2002), which have been implemented in the kernlab package of Karatzoglou et al. (2004).”
Blind Prediction of Natural Video Quality (2014, methodology): “The SVR based on the implementation in [48] was used to conduct quality score prediction.”
A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package (2008, methodology): “ear programs, the LpSolve R package [39] is used; strictly convex quadratic programs are solved using the Quadprog R package [40]; finally, not necessarily strictly convex quadratic programs are solved using the ipop routine of the Kernlab R package [41].”
Applied Predictive Modeling (2013, methodology): “A more comprehensive implementation of SVM models for regression is the kernlab package (Karatzoglou et al. 2004).”
On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, background): “According to [29], the 5 RBF kernel is a general purpose kernel well-suited for situations in which no 6 prior knowledge about the data is available.”
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data (2018, background): “All these kernels (including the RBF kernel) are classiﬁed as ”general purpose kernels” (Karatzoglou et al., 2004).”
CAMP: a useful resource for research on antimicrobial peptides (2009, methodology): “SVM implementation of Kernlab package in R-language was employed in this study (33,39).”