Improving Reliable Probabilistic Prediction by Using Additional Knowledge

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v60-nouretdinov17b, title = {Improving Reliable Probabilistic Prediction by Using Additional Knowledge}, author = {Nouretdinov, Ilia}, booktitle = {Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {193--200}, year = {2017}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Papadopoulos, Harris}, volume = {60}, series = {Proceedings of Machine Learning Research}, month = {13--16 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v60/nouretdinov17b/nouretdinov17b.pdf}, url = {https://proceedings.mlr.press/v60/nouretdinov17b.html}, 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.} }
Endnote
%0 Conference Paper %T Improving Reliable Probabilistic Prediction by Using Additional Knowledge %A Ilia Nouretdinov %B Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2017 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Harris Papadopoulos %F pmlr-v60-nouretdinov17b %I PMLR %P 193--200 %U https://proceedings.mlr.press/v60/nouretdinov17b.html %V 60 %X 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.
APA
Nouretdinov, I.. (2017). Improving Reliable Probabilistic Prediction by Using Additional Knowledge. Proceedings of the Sixth Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 60:193-200 Available from https://proceedings.mlr.press/v60/nouretdinov17b.html.

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