ordmvnorm

A data set that demonstrates the ordinal multiple-instance learning
structure with feature columns randomly sampled from a multivariate normal
distribution.

datasets

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

ordmvnorm function

An MI data frame with 1000 rows 8 variables, and 5 bags. Instance
labels can be accessed via <code>attr(ordmvnorm, "instance_label")</code>.<dl>
<dt>bag_label</dt>
<dd>outcome label at the bag level. This is the maximum of the <code>inst_label</code> for each bag</dd><dt>bag_name</dt>
<dd>indicator of each bag</dd><dt>V1</dt>
<dd>Variable with mean equal to <code>2 * inst_label</code></dd><dt>V2</dt>
<dd>Variable with mean equal to <code>-1 * inst_label</code></dd><dt>V3</dt>
<dd>Variable with mean equal to <code>1 * inst_label</code></dd><dt>V4</dt>
<dd>Variable with mean 0, essentially noise</dd><dt>V5</dt>
<dd>Variable with mean 0, essentially noise</dd>
</dl>

Format

Sample ordinal MIL data using mvnorm — ordmvnorm

An MI data frame with 1000 rows 8 variables, and 5 bags. Instance
labels can be accessed via <code>attr(ordmvnorm, "instance_label")</code>.<dl>
<dt>bag_label</dt>
<dd>outcome label at the bag level. This is the maximum of the <code>inst_label</code> for each bag</dd>

<dt>bag_name</dt>
<dd>indicator of each bag</dd>

<dt>V1</dt>
<dd>Variable with mean equal to <code>2 * inst_label</code></dd>

<dt>V2</dt>
<dd>Variable with mean equal to <code>-1 * inst_label</code></dd>

<dt>V3</dt>
<dd>Variable with mean equal to <code>1 * inst_label</code></dd>

<dt>V4</dt>
<dd>Variable with mean 0, essentially noise</dd>

<dt>V5</dt>
<dd>Variable with mean 0, essentially noise</dd>


</dl>

ordmvnorm: Sample ordinal MIL data using mvnorm

Description

Usage

Arguments

Format