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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  [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:
Downloads:
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.