Transfer learning for the probabilistic classification vector machine

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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.

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