Create an <code>mi</code> object, usually used as a response variable in a model
formula.

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 (2024) <doi:10.1214/24-AOAS1876>; 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.

Sean Kent

mildsvm

Multiple-Instance Learning with Support Vector Machines

Yifei Liou

mi function

<dl><dt>bag_label</dt>
<dd>The bag label or response, recorded as 0 = negative, 1 =
positive.</dd>
<dt>bag_name</dt>
<dd>A unique bag identifier for each instance.</dd></dl>

Arguments

Author

Create an mi object — mi

<dl>

<dt>bag_label</dt>
<dd>The bag label or response, recorded as 0 = negative, 1 =
positive.</dd>


<dt>bag_name</dt>
<dd>A unique bag identifier for each instance.</dd>

</dl>

mi: Create an `mi` object

Description

Usage

Value

Arguments

Author

See Also

Examples