Improving Reliable Probabilistic Prediction by Using Additional Knowledge

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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 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.
Original languageEnglish
Title of host publication6th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2017)
Pages193-200
Number of pages8
Volume60
Publication statusPublished - Jun 2017

Keywords

  • Venn machine, reliable probabilistic prediction, additional information, transfer

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