Transfer learning extensions for the probabilistic classification vector machine

NEUROCOMPUTING

卷:397

Article

Raab, Christoph¹  Schleif, Frank-Michael¹ 

[1] Univ Appl Sci Wurzburg Schweinfurt, Sanderheinrichsleitenweg 20, Wurzburg, Germany

关键词: Transfer learning;    Probabilistic classification vector machine;    Transfer kernel learning;    Nystrom approximation;    Basis transfer;    Sparsity;   

DOI  :  10.1016/j.neucom.2019.09.104

来源: Elsevier

【 摘 要 】

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 models are neither sparse nor interpretable. 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 propose two transfer learning extensions integrated into the sparse and interpretable probabilistic classification vector machine. They are compared to standard benchmarks in the field and show their relevance either by sparsity or performance improvements. (C) 2019 Elsevier B.V. All rights reserved.

【 授权许可】

Free