Self-labeling techniques for semi-supervised time series classification: an empirical study
An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Springer
2017
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/44845/ |