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Split knowledge transfer in learning under privileged information framework
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)
Statisticon AB.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
2019 (English)In: Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR , 2019, Vol. 105, p. 43-52Conference paper, Published paper (Refereed)
Abstract [en]

Learning Under Privileged Information (LUPI) enables the inclusion of additional (privileged) information when training machine learning models, data that is not available when making predictions. The methodology has been successfully applied to a diverse set of problems from various fields. SVM+ was the first realization of the LUPI paradigm which showed fast convergence but did not scale well. To address the scalability issue, knowledge transfer approaches were proposed to estimate privileged information from standard features in order to construct improved decision rules. Most available knowledge transfer methods use regression techniques and the same data for approximating the privileged features as for learning the transfer function. Inspired by the cross-validation approach, we propose to partition the training data into $K$ folds and use each fold for learning a transfer function and the remaining folds for approximations of privileged features—we refer to this as split knowledge transfer. We evaluate the method using four different experimental setups comprising one synthetic and three real datasets. The results indicate that our approach leads to improved accuracy as compared to LUPI with standard knowledge transfer.

Place, publisher, year, edition, pages
PMLR , 2019. Vol. 105, p. 43-52
Series
Proceedings of Machine Learning Research, ISSN 2640-3498
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-400587OAI: oai:DiVA.org:uu-400587DiVA, id: diva2:1381810
Conference
Conformal and Probabilistic Prediction and Applications
Funder
Swedish Foundation for Strategic Research , HASTEAvailable from: 2019-12-27 Created: 2019-12-27 Last updated: 2019-12-27

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Gauraha, NiharikaSpjuth, Ola

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • nn-NB
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Output format
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