Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.

Journal: Journal of neural engineering

Published Date: Apr 1, 2017

Abstract

OBJECTIVE: Classifier transfers usually come with dataset shifts. To overcome dataset shifts in practical applications, we consider the limitations in computational resources in this paper for the adaptation of batch learning algorithms, like the support vector machine (SVM).

Authors

Mario Michael Krell

Robotics Research Group, University of Bremen, Robert-Hooke-Str. 1, Bremen, Germany.
Nils Wilshusen
Anett Seeland
Su Kyoung Kim

Keywords

Algorithms Brain Brain-Computer Interfaces Computer Simulation Data Mining Electroencephalography Humans Models, Neurological Online Systems Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (28192282)

Classifier transfer with data selection strategies for online support vector machine classification with class imbalance.

Abstract

Authors

Keywords

External Resources

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