Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
A study on SMO-type decomposition methods for support vector machines
IEEE Transactions on Neural Networks
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One problem in current Brain-Computer Interfaces (BCIs) is non-stationarity of the underlying signals. This causes deteriorating performance throughout a session and difficulties to transfer a classifier from one session to another, which results in the need of collecting training data every session. Using an adaptive classifier is one solution to keep the performance stable and reduce the amount of training that is needed for a good BCI performance. In this paper we present an approach for an adaptive classifier based on a Support Vector Machine (SVM). We evaluate its advantage on offline BCI data and show its benefits and online feasibility in an online experiment using a MEG-based BCI with 10 subjects.