Title :
A Self-Training Semi-Supervised Support Vector Machine Algorithm and its Applications in Brain Computer Interface
Author :
Li, Yuanqing ; Li, Huiqi ; Guan, Cuntai ; Chin, Zhengyang
Author_Institution :
Inst. for Inforcomm Res.
Abstract :
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.
Keywords :
biology computing; brain models; iterative methods; learning (artificial intelligence); support vector machines; user interfaces; P300-based speller; brain computer interface; iterative algorithm; self-training semisupervised support vector machine algorithm; training effort; Algorithm design and analysis; Application software; Brain computer interfaces; Convergence; Data analysis; Iterative algorithms; Semisupervised learning; Support vector machines; Testing; Training data; P300; Supporter Vector Machine (SVM); brain computer interface (BCI); convergence; semi-supervised learning;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0727-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2007.366697
