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Transfer learning extensions for the probabilistic classification vector machine.
- Source :
-
Neurocomputing . Jul2020, Vol. 397, p320-330. 11p. - Publication Year :
- 2020
-
Abstract
- • Adopting Invariant Kernel Transfer Learning for sparse models. • Nyström based Basis Transfer for performance improvements by means of prediction and computational time. • A variety of experimental results is provided showing the efficiency of the proposed approach. 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE processing
*SUPERVISED learning
*PROCESS mining
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 397
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 143310182
- Full Text :
- https://doi.org/10.1016/j.neucom.2019.09.104