| Class | Description |
|---|---|
| CitationKNN |
Modified version of the Citation kNN multi instance classifier.
For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach. |
| MDD |
Modified Diverse Density algorithm, with collective assumption.
More information about DD: Oded Maron (1998). |
| MIBoost |
MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.
For more information about Adaboost, see: Yoav Freund, Robert E. |
| MIDD |
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). |
| MIEMDD |
EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm.
It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM. |
| MILR |
Uses either standard or collective multi-instance assumption, but within linear regression.
|
| MINND |
Multiple-Instance Nearest Neighbour with Distribution learner.
It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0. |
| MIOptimalBall |
This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
|
| MISMO |
Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.
This implementation globally replaces all missing values and transforms nominal attributes into binary ones. |
| MISVM |
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
|
| MIWrapper |
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see: E. |
| SimpleMI |
Reduces MI data into mono-instance data.
|