The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classifier geometrical characteristic comparison and its application in classifier selection
Pattern Recognition Letters
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
Editorial: Brain decoding: Opportunities and challenges for pattern recognition
Pattern Recognition
Online semi-supervised ensemble updates for fMRI data
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
A survey of multiple classifier systems as hybrid systems
Information Fusion
Learning ensemble classifiers via restricted Boltzmann machines
Pattern Recognition Letters
Pattern Recognition Letters
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Functional magnetic resonance imaging (fMRI) provides a spatially accurate measure of brain activity. Real-time classification allows the use of fMRI in neurofeedback experiments. With limited labelled data available, a fixed pre-trained classifier may be inaccurate. We propose that streaming fMRI data may be classified using a classifier ensemble which is updated through naive labelling. Naive labelling is a protocol where in the absence of ground truth, updates are carried out using the label assigned by the classifier. We perform experiments on three fMRI datasets to demonstrate that naive labelling is able to improve upon a pre-trained initial classifier.