## Package milk.classifiers

Interface Summary
MITransform: Interface to transform any MI data into data suitable for mono-instance learner
MIUpdateableClassifier: Interface to incremental classification models that can learn using one exemplar at a time.

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