Self-labeling techniques for semi-supervised time series classification: an empirical study

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...

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Bibliographic Details
Main Authors: González, Mabel, Bergmeir, Christoph, Triguero, Isaac, Rodríguez, Yanet, Benítez, José M.
Format: Article
Published: Springer 2017
Subjects:
Semi-supervised classification; Self-labeled; Time series classification; Semi-supervised learning; Self-training
Online Access:https://eprints.nottingham.ac.uk/44845/

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