Journal
PATTERN RECOGNITION LETTERS
Volume 29, Issue 9, Pages 1285-1294Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2008.01.030
Keywords
semi-supervised support vector machine (SVM); model selection; convergence; brain computer interface (BCI); electroencephalogram (EEG)
Categories
Ask authors/readers for more resources
In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a data set collected from a P300-based brain computer interface (BCI) speller. This algorithm is shown to be able to significantly reduce training effort of the P300-based BCI speller. (c) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available