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) <arXiv:2206.14704>; Xiao, Liu, and Hao (2018) <doi:10.1109/TNNLS.2017.2766164>; Muandet et al. (2012) <>; Chu and Keerthi (2007) <doi:10.1162/neco.2007.19.3.792>; and Andrews et al. (2003) <>. 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 <> 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
Author: Sean Kent ORCID iD [aut, cre], Yifei Liou [aut]
Maintainer: Sean Kent <skent259 at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: mildsvm results


Reference manual: mildsvm.pdf


Package source: mildsvm_0.4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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