Hyperparameter selection for high dimensional sparse learning : application to neuroimaging / Sélection d'hyperparamètres pour l'apprentissage parcimonieux en grande dimension : application à la neuroimagerie
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Tác giả chưa xác định. Hyperparameter selection for high dimensional sparse learning : application to neuroimaging / Sélection d'hyperparamètres pour l'apprentissage parcimonieux en grande dimension : application à la neuroimagerie. Thesis, HAL - Pháp.
Việt Nam (chuẩn TCVN 5453:1991):
. Hyperparameter selection for high dimensional sparse learning : application to neuroimaging / Sélection d'hyperparamètres pour l'apprentissage parcimonieux en grande dimension : application à la neuroimagerie. Thesis. HAL - Pháp. Truy cập từ .
Tóm tắt
Due to non-invasiveness and excellent time resolution, magneto- and electroencephalography (M/EEG) have emerged as tools of choice to monitor brain activity. Reconstructing brain signals from M/EEG measurements can be cast as a high dimensional ill-posed inverse problem. Typical estimators of brain signals involve challenging optimization problems, composed of the sum of a data-fidelity term, and a sparsity promoting term. Because of their notoriously hard to tune regularization hyperparameters,...