| Information theoretic learning: Renyi's entropy and kernel perspectives JC Principe Springer Science & Business Media, 2010 | 922 | 2010 |
| An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems D Erdogmus, JC Principe IEEE Transactions on Signal Processing 50 (7), 1780-1786, 2002 | 360 | 2002 |
| The future of human-in-the-loop cyber-physical systems G Schirner, D Erdogmus, K Chowdhury, T Padir Computer 46 (1), 36-45, 2013 | 332 | 2013 |
| Generalized information potential criterion for adaptive system training D Erdogmus, JC Principe IEEE Transactions on Neural Networks 13 (5), 1035-1044, 2002 | 272 | 2002 |
| Tversky loss function for image segmentation using 3D fully convolutional deep networks SSM Salehi, D Erdogmus, A Gholipour International workshop on machine learning in medical imaging, 379-387, 2017 | 271 | 2017 |
| Optimizing the P300-based brain–computer interface: current status, limitations and future directions JN Mak, Y Arbel, JW Minett, LM McCane, B Yuksel, D Ryan, D Thompson, ... Journal of neural engineering 8 (2), 025003, 2011 | 234 | 2011 |
| Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks JM Brown, JP Campbell, A Beers, K Chang, S Ostmo, RVP Chan, J Dy, ... JAMA ophthalmology 136 (7), 803-810, 2018 | 224 | 2018 |
| Locally defined principal curves and surfaces U Ozertem, D Erdogmus The Journal of Machine Learning Research 12, 1249-1286, 2011 | 214 | 2011 |
| Blind source separation using Renyi's mutual information KE Hild, D Erdogmus, J Príncipe IEEE Signal Processing Letters 8 (6), 174-176, 2001 | 196 | 2001 |
| Feature extraction using information-theoretic learning KE Hild, D Erdogmus, K Torkkola, JC Principe IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (9), 1385-1392, 2006 | 195 | 2006 |
| Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging SSM Salehi, D Erdogmus, A Gholipour IEEE transactions on medical imaging 36 (11), 2319-2330, 2017 | 168 | 2017 |
| A comparison of optimal MIMO linear and nonlinear models for brain–machine interfaces SP Kim, JC Sanchez, YN Rao, D Erdogmus, JM Carmena, MA Lebedev, ... Journal of neural engineering 3 (2), 145, 2006 | 151 | 2006 |
| Clustering using Renyi's entropy R Jenssen, KE Hild, D Erdogmus, JC Principe, T Eltoft Proceedings of the International Joint Conference on Neural Networks, 2003 …, 2003 | 137 | 2003 |
| A novel LMS algorithm applied to adaptive noise cancellation JM Gorriz, J Ramírez, S Cruces-Alvarez, CG Puntonet, EW Lang, ... IEEE Signal Processing Letters 16 (1), 34-37, 2008 | 127 | 2008 |
| Feature selection in MLPs and SVMs based on maximum output information V Sindhwani, S Rakshit, D Deodhare, D Erdogmus, JC Principe, P Niyogi IEEE transactions on neural networks 15 (4), 937-948, 2004 | 119 | 2004 |
| SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging EG Larsson, D Erdogmus, R Yan, JC Principe, JR Fitzsimmons Journal of Magnetic Resonance 163 (1), 121-123, 2003 | 117 | 2003 |
| The Cauchy–Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels R Jenssen, JC Principe, D Erdogmus, T Eltoft Journal of the Franklin Institute 343 (6), 614-629, 2006 | 114 | 2006 |
| RSVP keyboard: An EEG based typing interface U Orhan, KE Hild, D Erdogmus, B Roark, B Oken, M Fried-Oken 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 113 | 2012 |
| From linear adaptive filtering to nonlinear information processing-the design and analysis of information processing systems D Erdogmus, JC Principe IEEE Signal Processing Magazine 23 (6), 14-33, 2006 | 113 | 2006 |
| Information theoretic learning: Renyi's entropy and its applications to adaptive system training D Erdogmus University of Florida, 2002 | 106 | 2002 |
jose principeprofessor of ECE, U. of FloridaVerified email at cnel.ufl.edu
