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.
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