Improving Reliable Probabilistic Prediction by Using Additional Knowledge

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Ilia Nouretdinov ;
Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 60:193-200, 2017.

Abstract

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 make use of this information by making a taxonomy transfer, where taxonomy is the core detail of Venn Machine framework. The transfer is done from the examples with additional information to the examples without additional information.

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