* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries.
* afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries.
* extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information.
* extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes.
* sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries.
* ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module.
?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module.
?knn.m: Matlab script implementation of the k-Nearest Neighbor Classifier Module.
This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox