Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
| Version: | 1.2.4 |
| Depends: | R (≥ 2.12.0), methods |
| Suggests: | knitr, rmarkdown, deldir, testthat |
| Enhances: | mlr, ParamHelpers |
| Published: | 2019-09-14 |
| Author: | Ingo Steinwart, Philipp Thomann |
| Maintainer: | Philipp Thomann <philipp.thomann at mathematik.uni-stuttgart.de> |
| License: | AGPL-3 |
| Copyright: | Ingo Steinwart, Philipp Thomann, Mohammad Farooq |
| URL: | https://github.com/liquidSVM/liquidSVM |
| NeedsCompilation: | yes |
| Citation: | liquidSVM citation info |
| Materials: | README |
| CRAN checks: | liquidSVM results |
| Reference manual: | liquidSVM.pdf |
| Vignettes: |
liquidSVM Demo liquidSVM Documentation |
| Package source: | liquidSVM_1.2.4.tar.gz |
| Windows binaries: | r-devel: liquidSVM_1.2.4.zip, r-release: liquidSVM_1.2.4.zip, r-oldrel: liquidSVM_1.2.4.zip |
| macOS binaries: | r-release: liquidSVM_1.2.4.tgz, r-oldrel: liquidSVM_1.2.4.tgz |
| Old sources: | liquidSVM archive |
| Reverse suggests: | parsnip |
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