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Authors: Mario Michael Krell 1 ; Nils Wilshusen 1 ; Andrei Cristian Ignat 2 and Su Kyoung Kim 3

Affiliations: 1 University of Bremen, Germany ; 2 Robotics Innovation Center, German Research Center for Artificial Intelligence GmbH and UC Santa Cruz, Germany ; 3 Robotics Innovation Center and German Research Center for Artificial Intelligence GmbH, Germany

ISBN: 978-989-758-161-8

Keyword(s): Support Vector Machine, Online Learning, Brain Computer Interface, Electroencephalogram, Incremental/Decremental Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Instruments and Devices ; Biomedical Signal Processing ; Brain-Computer Interfaces ; Data Manipulation ; Devices ; EMG Signal Processing and Applications ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Mobile and Embedded Devices ; Neural Rehabilitation ; Neural Signal Processing ; Neurocomputing ; NeuroSensing and Diagnosis ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Robotic Assisted Therapy ; Sensor Networks ; Soft Computing

Abstract: It is often the case that practical applications of support vector machines (SVMs) require the capability to perform online learning under limited availability of computational resources. Enabling SVMs for online learning can be done through several strategies. One group thereof manipulates the training data and limits its size. We aim to summarize these existing approaches and compare them, firstly, on several synthetic datasets with different shifts and, secondly, on electroencephalographic (EEG) data. During the manipulation, class imbalance can occur across the training data and it might even happen that all samples of one class are removed. In order to deal with this potential issue, we suggest and compare three balancing criteria.

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Paper citation in several formats:
Michael Krell, M.; Wilshusen, N.; Wilshusen, N.; Cristian Ignat, A.; Cristian Ignat, A. and Kyoung Kim, S. (2015). Comparison of Data Selection Strategies for Online Support Vector Machine Classification.In Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-161-8, pages 59-67. DOI: 10.5220/0005650700590067

@conference{neurotechnix15,
author={Mario Michael Krell. and Nils Wilshusen. and Nils Wilshusen. and Andrei Cristian Ignat. and Andrei Cristian Ignat. and Su Kyoung Kim.},
title={Comparison of Data Selection Strategies for Online Support Vector Machine Classification},
booktitle={Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2015},
pages={59-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005650700590067},
isbn={978-989-758-161-8},
}

TY - CONF

JO - Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Comparison of Data Selection Strategies for Online Support Vector Machine Classification
SN - 978-989-758-161-8
AU - Michael Krell, M.
AU - Wilshusen, N.
AU - Wilshusen, N.
AU - Cristian Ignat, A.
AU - Cristian Ignat, A.
AU - Kyoung Kim, S.
PY - 2015
SP - 59
EP - 67
DO - 10.5220/0005650700590067

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