Depuration, augmentation and balancing of training data for supervised learning based detectors of EEG patterns
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Andreas Schulze-Bonhage | Thomas Stieglitz | Matthias Dümpelmann | Julia Jacobs | Daniel Lachner Piza | A. Schulze-Bonhage | T. Stieglitz | J. Jacobs | M. Dümpelmann | Daniel Lachner Piza
[1] Jean Gotman,et al. Improving the identification of High Frequency Oscillations , 2009, Clinical Neurophysiology.
[2] Hassan H. Malik,et al. Automatic Training Data Cleaning for Text Classification , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.
[3] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[4] Foster Provost,et al. The effect of class distribution on classifier learning: an empirical study , 2001 .
[5] Eduardo Gasca,et al. Decontamination of Training Samples for Supervised Pattern Recognition Methods , 2000, SSPR/SPR.
[6] Poul Jennum,et al. Inter-expert and intra-expert reliability in sleep spindle scoring , 2015, Clinical Neurophysiology.
[7] Davis E. King,et al. Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..
[8] Thierry Dutoit,et al. Automatic sleep spindles detection — Overview and development of a standard proposal assessment method , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[9] C. O’Reilly,et al. Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research , 2014, Journal of sleep research.