期刊
PATTERN RECOGNITION LETTERS
卷 29, 期 9, 页码 1285-1294出版社
ELSEVIER
DOI: 10.1016/j.patrec.2008.01.030
关键词
semi-supervised support vector machine (SVM); model selection; convergence; brain computer interface (BCI); electroencephalogram (EEG)
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.
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