Transfer learning for the probabilistic classification vector machine

Christoph Raab, Frank-Michael Schleif
Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, PMLR 91:187-200, 2018.

Abstract

Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning methods are not well suited and used for sparse and interpretable models. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we show how transfer learning can be integrated into the sparse and interpretable probabilistic classification vector machine and it is compared with different standard benchmarks in the field.

Cite this Paper

BibTeX
@InProceedings{pmlr-v91-raab18a, title = {Transfer learning for the probabilistic classification vector machine}, author = {Raab, Christoph and Schleif, Frank-Michael}, booktitle = {Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications}, pages = {187--200}, year = {2018}, editor = {Gammerman, Alex and Vovk, Vladimir and Luo, Zhiyuan and Smirnov, Evgueni and Peeters, Ralf}, volume = {91}, series = {Proceedings of Machine Learning Research}, month = {11--13 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v91/raab18a/raab18a.pdf}, url = {https://proceedings.mlr.press/v91/raab18a.html}, abstract = {Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning methods are not well suited and used for sparse and interpretable models. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we show how transfer learning can be integrated into the sparse and interpretable probabilistic classification vector machine and it is compared with different standard benchmarks in the field.} }
Endnote
%0 Conference Paper %T Transfer learning for the probabilistic classification vector machine %A Christoph Raab %A Frank-Michael Schleif %B Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications %C Proceedings of Machine Learning Research %D 2018 %E Alex Gammerman %E Vladimir Vovk %E Zhiyuan Luo %E Evgueni Smirnov %E Ralf Peeters %F pmlr-v91-raab18a %I PMLR %P 187--200 %U https://proceedings.mlr.press/v91/raab18a.html %V 91 %X Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning methods are not well suited and used for sparse and interpretable models. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we show how transfer learning can be integrated into the sparse and interpretable probabilistic classification vector machine and it is compared with different standard benchmarks in the field.
APA
Raab, C. & Schleif, F.. (2018). Transfer learning for the probabilistic classification vector machine. Proceedings of the Seventh Workshop on Conformal and Probabilistic Prediction and Applications, in Proceedings of Machine Learning Research 91:187-200 Available from https://proceedings.mlr.press/v91/raab18a.html.

Related Material