In this paper, we analyze the convergence of an iterative self-training semi-supervised support vector machine (SVM) algorithm, which is designed for classification in small training data case. This algorithm converges fast and has low computational burden. Its effectiveness is also demonstrated by our data analysis results. Furthermore, we illustrate that this algorithm can be used to significantly reduce training effort and improve adaptability of a brain computer interface (BCI) system, a P300-based speller.
['Yuanqing Li', 'Huiqi Li', 'Cuntai Guan', 'Zhengyang Chin']
A Self-Training Semi-Supervised Support Vector Machine Algorithm and its Applications in Brain Computer Interface