no code implementations • 20 Jun 2018 • Justus T. C. Schwabedal, John C. Snyder, Ayse Cakmak, Shamim Nemati, Gari. D. Clifford
To quantify how well the distributions of the surrogates and the original data match, we evaluated a trained classifier on surrogates of correctly classified examples, and summarized these conditional predictions in a confusion matrix.
1 code implementation • 7 May 2018 • Supreeth P. Shashikumar, Amit J. Shah, Gari. D. Clifford, Shamim Nemati
We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance.
no code implementations • 1 Dec 2017 • Samaneh Nasiri Ghosheh Bolagh, Gari. D. Clifford
Inter-subject variability between individuals poses a challenge in inter-subject brain signal analysis problems.
no code implementations • 23 Mar 2015 • Tingting Zhu, Nic Dunkley, Joachim Behar, David A. Clifton, Gari. D. Clifford
To address these problems, a Bayesian Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm.