MISVM
Package
weka.classifiers.mi
Synopsis
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). Applying weka.classifiers.functions.SMO to solve multiple instances problem.
The algorithm first assign the bag label to each instance in the bag as its initial class label. After that applying SMO to compute SVM solution for all instances in positive bags And then reassign the class label of each instance in the positive bag according to the SVM result Keep on iteration until labels do not change anymore.
For more information see:
Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann: Support Vector Machines for Multiple-Instance Learning. In: Advances in Neural Information Processing Systems 15, 561-568, 2003.
Options
The table below describes the options available for MISVM.
Option |
Description |
|---|---|
c |
The value for C. |
debug |
If set to true, classifier may output additional info to the console. |
filterType |
The filter type for transforming the training data. |
kernel |
The kernel to use. |
maxIterations |
The maximum number of iterations to perform. |
Capabilities
The table below describes the capabilites of MISVM.
Capability |
Supported |
|---|---|
Class |
Missing class values, Binary class |
Attributes |
Unary attributes, Nominal attributes, Relational attributes, Binary attributes, Empty nominal attributes, Missing values |
Other |
Only multi-Instance data |
Min # of instances |
1 |