Classifier ensembles for fMRI data analysis: an experiment.

@article{Kuncheva2010ClassifierEF,
  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},
  year={2010},
  volume={28 4},
  pages={583-93}
}
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
40 Citations
58 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 40 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 58 references

P

  • J. V. Haxby, M. Gobbini, M. L. Furey, A. Ishal, J. L. Schouten
  • Pietrini, Distributed and overlapping…
  • 2001
Highly Influential
5 Excerpts

Neural Networks for Pattern Recognition

  • C. Bishop
  • Clarendon Press, Oxford
  • 1995
Highly Influential
6 Excerpts

Ensemble learning

  • G. Brown
  • in: C. Sammut, G. Webb (Eds.), In Encyclopedia of…
  • 2009
1 Excerpt

Similar Papers

Loading similar papers…