Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 1285-1294 |
Number of pages | 10 |
Journal | Pattern Recognition Letters |
Volume | 29 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1 Jul 2008 |
Externally published | Yes |
Keywords
- 10
- 170
- 90
- Brain computer interface (BCI)
- Convergence
- Electroencephalogram (EEG)
- Model selection
- Semi-supervised support vector machine (SVM)