| An improved P300-based brain-computer interface H Serby, E Yom-Tov, GF Inbar IEEE Transactions on neural systems and rehabilitation engineering 13 (1), 89-98, 2005 | 453 | 2005 |
| Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval E Yom-Tov, S Fine, D Carmel, A Darlow Proceedings of the 28th annual international ACM SIGIR conference on …, 2005 | 247 | 2005 |
| What makes a query difficult? D Carmel, E Yom-Tov, A Darlow, D Pelleg Proceedings of the 29th annual international ACM SIGIR conference on …, 2006 | 240 | 2006 |
| Models of user engagement J Lehmann, M Lalmas, E Yom-Tov, G Dupret International conference on user modeling, adaptation, and personalization …, 2012 | 220 | 2012 |
| A streaming parallel decision tree algorithm Y Ben-Haim, E Tom-Tov Journal of Machine Learning Research 11 (Feb), 849-872, 2010 | 201 | 2010 |
| Milepost gcc: Machine learning enabled self-tuning compiler G Fursin, Y Kashnikov, AW Memon, Z Chamski, O Temam, M Namolaru, ... International journal of parallel programming 39 (3), 296-327, 2011 | 189 | 2011 |
| Predicting customer churn in mobile networks through analysis of social groups Y Richter, E Yom-Tov, N Slonim Proceedings of the 2010 SIAM international conference on data mining, 732-741, 2010 | 185 | 2010 |
| Estimating the query difficulty for information retrieval D Carmel, E Yom-Tov Synthesis Lectures on Information Concepts, Retrieval, and Services 2 (1), 1-89, 2010 | 164 | 2010 |
| Classification of finger activation for use in a robotic prosthesis arm D Peleg, E Braiman, E Yom-Tov, GF Inbar IEEE Transactions on Neural Systems and Rehabilitation Engineering 10 (4 …, 2002 | 145 | 2002 |
| Measuring user engagement M Lalmas, H O'Brien, E Yom-Tov Synthesis Lectures on Information Concepts, Retrieval, and Services 6 (4), 1-132, 2014 | 137 | 2014 |
| MILEPOST GCC: machine learning based research compiler G Fursin, C Miranda, O Temam, M Namolaru, E Yom-Tov, A Zaks, ... | 128 | 2008 |
| Feature selection for the classification of movements from single movement-related potentials E Yom-Tov, GF Inbar IEEE Transactions on neural systems and rehabilitation engineering 10 (3 …, 2002 | 108 | 2002 |
| Automatic churn prediction S Fine, E Yom-Tov, Y Richter US Patent App. 12/790,850, 2011 | 98 | 2011 |
| Postmarket drug surveillance without trial costs: discovery of adverse drug reactions through large-scale analysis of web search queries E Yom-Tov, E Gabrilovich Journal of medical Internet research 15 (6), e124, 2013 | 97 | 2013 |
| Computer manual in MATLAB to accompany pattern classification DG Stork, E Yom-Tov Wiley-Interscience, 2004 | 85 | 2004 |
| Method and system for scheduling of jobs Y Aridor, RE Harper, E Yom-Tov US Patent 7,958,509, 2011 | 82 | 2011 |
| Pro-anorexia and pro-recovery photo sharing: a tale of two warring tribes E Yom-Tov, L Fernandez-Luque, I Weber, SP Crain Journal of medical Internet research 14 (6), e151, 2012 | 76 | 2012 |
| Microsoft malware classification challenge R Ronen, M Radu, C Feuerstein, E Yom-Tov, M Ahmadi arXiv preprint arXiv:1802.10135, 2018 | 74 | 2018 |
| Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface E Yom-Tov, GF Inbar Medical and Biological Engineering and Computing 41 (1), 85-93, 2003 | 69 | 2003 |
| Pascal large scale learning challenge S Sonnenburg, V Franc, E Yom-Tov, M Sebag | 62* | 2008 |
David CarmelPrincipal Applied Scientist, Amazon, HaifaVerified email at amazon.com
