|
Class Summary |
| DD |
Re-implement the Diverse Density algorithm, changes the testing procedure. |
| MDD |
Modified DD, with collective assumption |
| MIBoost |
MI AdaBoost method, consider 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. |
| MIClassifier |
Abstract classifier. |
| MIEvaluation |
Class for evaluating machine learning models. |
| MILR |
Use MI assumption but within LR
Valid options are: |
| MILRARITH |
Using collective assumption, arithmatic average of the posteriors of
instances inside a bag is taken. |
| MILRGEOM |
Normalized goemetric mean is taken on the posteriors of instances,
regardless of class labels
Valid options are: |
| MINND |
0657.591B Dissertation
Multiple-Instance Nearest Neighbour with Distribution learner . |
| MIRBFNetwork |
Multi-instance RBF network. |
| MIWrapper |
Weighted Wrapper method from Eibe
Valid options are: |
| SimpleMI |
Avg of feature data and reduce MI data into mono-instance
Valid options are: |
| TLD |
0657.594 Thesis
Two-Level Distribution approach, changes the
starting value of the searching algorithm, supplement the cut-off
modification and check missing values. |
| TLDSimple |
0657.594 Thesis
A simpler version of TLD, \mu random but \sigma^2 fixed and estimated via data |