Adaptive schemes applied to online SVM for BCI data classification.
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.
Oskoei
Mohammadreza Asghari
MA
School of CS and EE, University of Essex, UK. masgha@essex.ac.uk
Gan
John Q
JQ
Hu
Huosheng
H
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000295:instrumentation* / Q000379:methods; D019295:Computational Biology / Q000379:methods; D004867:Equipment Design; D017143:Fuzzy Logic; D006801:Humans; D020407:Internet; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D014584:User-Computer Interface