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Adaptive schemes applied to online SVM for BCI data classification

Oskoei, MA and Gan, JQ and Hu, O (2009) 'Adaptive schemes applied to online SVM for BCI data classification.' Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009. 2600 - 2603.

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Abstract

This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate. ©2009 IEEE.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 12 Dec 2012 21:09
Last Modified: 23 Jan 2019 00:15
URI: http://repository.essex.ac.uk/id/eprint/4144

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