| weka.classifiers.mi |
|
| Java Source File Name | Type | Comment |
| CitationKNN.java | Class |
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.java | Class |
Modified Diverse Density algorithm, with collective assumption.
More information about DD:
Oded Maron (1998). |
| MIBoost.java | Class |
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.java | Class |
Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998). |
| MIEMDD.java | Class |
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.java | Class |
Uses either standard or collective multi-instance assumption, but within linear regression. |
| MINND.java | Class |
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.java | Class |
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.java | Class |
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.java | Class |
Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). |
| MIWrapper.java | Class |
A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E. |
| SimpleMI.java | Class |
Reduces MI data into mono-instance data. |
| TLD.java | Class |
Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values.
For more information see:
Xin Xu (2003). |
| TLDSimple.java | Class |
A simpler version of TLD, mu random but sigma^2 fixed and estimated via data.
For more information see:
Xin Xu (2003). |