The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

mildsvm: Multiple-Instance Learning with Support Vector Machines

Weakly supervised (WS), multiple instance (MI) data lives in numerous interesting applications such as drug discovery, object detection, and tumor prediction on whole slide images. The 'mildsvm' package provides an easy way to learn from this data by training Support Vector Machine (SVM)-based classifiers. It also contains helpful functions for building and printing multiple instance data frames. The core methods from 'mildsvm' come from the following references: Kent and Yu (2022) <doi:10.48550/arXiv.2206.14704>; Xiao, Liu, and Hao (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012) <https://proceedings.neurips.cc/paper/2012/file/9bf31c7ff062936a96d3c8bd1f8f2ff3-Paper.pdf>; Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews et al. (2003) <https://papers.nips.cc/paper/2232-support-vector-machines-for-multiple-instance-learning.pdf>. Many functions use the 'Gurobi' optimization back-end to improve the optimization problem speed; the 'gurobi' R package and associated software can be downloaded from <https://www.gurobi.com> after obtaining a license.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: dplyr, e1071, kernlab, magrittr, mvtnorm, pillar, pROC, purrr, rlang, stats, tibble, tidyr, utils
Suggests: covr, gurobi, Matrix, testthat
Published: 2022-07-14
DOI: 10.32614/CRAN.package.mildsvm
Author: Sean Kent ORCID iD [aut, cre], Yifei Liou [aut]
Maintainer: Sean Kent <skent259 at gmail.com>
BugReports: https://github.com/skent259/mildsvm/issues
License: MIT + file LICENSE
URL: https://github.com/skent259/mildsvm
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mildsvm results

Documentation:

Reference manual: mildsvm.pdf

Downloads:

Package source: mildsvm_0.4.0.tar.gz
Windows binaries: r-devel: mildsvm_0.4.0.zip, r-release: mildsvm_0.4.0.zip, r-oldrel: mildsvm_0.4.0.zip
macOS binaries: r-release (arm64): mildsvm_0.4.0.tgz, r-oldrel (arm64): mildsvm_0.4.0.tgz, r-release (x86_64): mildsvm_0.4.0.tgz, r-oldrel (x86_64): mildsvm_0.4.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mildsvm to link to this page.

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.
Health stats visible at Monitor.