Improving Reliable Probabilistic Prediction by Using Additional Knowledge

Venn Machine is a recently developed machine learning framework for reliable probabilistic prediction of the labels for new examples/. This work proposes a way to extend Venn machine to the framework known as Learning Under Privileged Information: some additional features are available for a part of the training set, and are missing for the example being predicted. We suggest obtaining use from this information by making a it taxonomy transfer where taxonomy is the core detail of Venn Machine framework so that the transfer is done from the examples with additional information to the examples without additional information.