Oskoei Mohammadreza Asghari, Gan John Q, Hu Huosheng
School of CS and EE, University of Essex, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2600-3. doi: 10.1109/IEMBS.2009.5335328.
This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate.
本文评估了应用于对脑机接口（BCI）数据进行分类的在线支持向量机（SVM）的有监督和无监督自适应方案。在线支持向量机在新样本到来时对其进行处理，并更新现有支持向量，而无需参考先前的样本。结果表明，在线支持向量机的性能与标准支持向量机相似，并且有监督和无监督方案均提高了分类命中率。
