How to Learn?
Disjoint sets of labeled data, generated during a training session, will be created.
Decision trees will be learned on each of the disjoint data sets in parallel and converted to rules.
The rules will be a reflection of the saliencies selected by the user.
The rules will be combined together into a single model. This rule model will be the learned representation of regions likely to interest the user.