TY  - BOOK
AB  - Real-world machine learning applications must be able to adapt to systematic changes in the data, e.g. a new subject or sensor displacement. This can be seen as a form of transfer learning, where the goal is to reuse the old (source) model by adapting the new (target) data. This is a challenging task, if no labels for the target data are available. Here, we propose to use the structure of the source and target data to find a transformation from the source to target space in an unsupervised manner. Our preliminary experiments on multivariate time series data show the feasibility of the approach, but also its limits.
DA  - 2017
KW  - domain adaptation
KW  - transductive transfer lerning
KW  - time series classification
KW  - predictive modelling
KW  - echo state networks
LA  - eng
PY  - 2017
T2  - Proceedings of the Workshop on New Challenges in Neural Computation (NC2)
TI  - Unsupervised Transfer Learning for Time Series via Self-Predictive Modelling - First Results
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29141415
Y2  - 2024-11-22T09:23:29
ER  -