A Self-Training Semi-Supervised Support Vector Machine Algorithm and its Applications in Brain Computer Interface

In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation 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 signi cantly reduce training effort and improve adaptability of a brain computer interface (BCI) system, a P300-based speller.

• Type: Conference
• Year: 2007
• Where: ICASSP
• Authors: Yuanqing Li, Huiqi Li, Cuntai Guan, Zhengyang Chin
• Keywords: ICASSP 2007, Low Computational Burden, Semi-Supervised Support Vector, Signal Processing, Training Data Case