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We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | quadprog, svmpath |
| Published: | 2018-10-14 |
| Author: | Seung Jun Shin [aut, cre], Do Hyun Kim [aut] |
| Maintainer: | Seung Jun Shin <sjshin at korea.ac.kr> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | rocsvm.path results |
| Reference manual: | rocsvm.path.pdf |
| Package source: | rocsvm.path_0.1.0.tar.gz |
| Windows binaries: | r-devel: rocsvm.path_0.1.0.zip, r-release: rocsvm.path_0.1.0.zip, r-oldrel: rocsvm.path_0.1.0.zip |
| macOS binaries: | r-release (arm64): rocsvm.path_0.1.0.tgz, r-oldrel (arm64): rocsvm.path_0.1.0.tgz, r-release (x86_64): rocsvm.path_0.1.0.tgz, r-oldrel (x86_64): rocsvm.path_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.