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Split knowledge transfer in learning under privileged information framework
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Spjuth)
Statisticon AB.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap.ORCID-id: 0000-0002-8083-2864
2019 (engelsk)Inngår i: Proceedings of the Eighth Symposium on Conformal and Probabilistic Prediction and Applications, PMLR , 2019, Vol. 105, s. 43-52Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
PMLR , 2019. Vol. 105, s. 43-52
Serie
Proceedings of Machine Learning Research, ISSN 2640-3498
HSV kategori
Forskningsprogram
Statistik
Identifikatorer
URN: urn:nbn:se:uu:diva-400587OAI: oai:DiVA.org:uu-400587DiVA, id: diva2:1381810
Konferanse
Conformal and Probabilistic Prediction and Applications, 9-11 September, 2019, Golden Sands, Bulgaria
Forskningsfinansiär
Swedish Foundation for Strategic Research , HASTETilgjengelig fra: 2019-12-27 Laget: 2019-12-27 Sist oppdatert: 2020-06-05bibliografisk kontrollert

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Electronic full texthttp://proceedings.mlr.press/v105/gauraha19a.htmlhttps://cml.rhul.ac.uk/copa2019/

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