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.

Volume

1

fYear

2007

fDate

15-20 April 2007

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on

Conference_Location

Honolulu, HI

ISSN

1520-6149

Print_ISBN

1-4244-0727-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2007.366697

Filename

4217097