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Adaptive schemes applied to online SVM for BCI data classification

Oskoei, MA and Gan, JQ and Huosheng Hu, (2009) 'Adaptive schemes applied to online SVM for BCI data classification.' 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. pp. 2600-2603. ISSN 1557-170X

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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
Uncontrolled Keywords: Brain; Humans; Brain Mapping; Models, Statistical; Reproducibility of Results; Equipment Design; Computational Biology; Algorithms; Fuzzy Logic; Artificial Intelligence; Internet; Signal Processing, Computer-Assisted; Software; User-Computer Interface; Pattern Recognition, Automated
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 12 Dec 2012 21:09
Last Modified: 15 Jan 2022 00:23
URI: http://repository.essex.ac.uk/id/eprint/4144

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