Classifier ensembles for fMRI data analysis: an experiment.

  title={Classifier ensembles for fMRI data analysis: an experiment.},
  author={Ludmila I. Kuncheva and Juan Jos{\'e} Saame{\~n}o Rodr{\'i}guez},
  journal={Magnetic resonance imaging},
  volume={28 4},
Functional magnetic resonance imaging (fMRI) is becoming a forefront brain-computer interface tool. To decipher brain patterns, fast, accurate and reliable classifier methods are needed. The support vector machine (SVM) classifier has been traditionally used. Here we argue that state-of-the-art methods from pattern recognition and machine learning, such as classifier ensembles, offer more accurate classification. This study compares 18 classification methods on a publicly available real data… CONTINUE READING
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