Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Adaptive Learning Rate for Online Linear Discriminant Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
On-Line fMRI Data Classification Using Linear and Ensemble Classifiers
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Brain Computer Interface for Communication Using Real-Time fMRI
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Online semi-supervised ensemble updates for fMRI data
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Brain activation detection by neighborhood one-class SVM
Cognitive Systems Research
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Real time classification of fMRI data allows for neurofeedback experiments, whereby stimuli are updated in accordance with the response of the brain. In order to better facilitate real time fMRI classification, we propose a random subspace ensemble of online linear classifiers. In the absence of true class labels, classifiers are updated using the 'naive' label - the label predicted by the classifier. We propose three new ensemble update strategies, using the ensemble decision for updates. Our methods are tested on two emotion based fMRI data sets. We show that the best results are produced by an ensemble which updates using the ensemble decision, constrained by ensemble confidence.