| The relationship between Precision-Recall and ROC curves J Davis, M Goadrich Proceedings of the 23rd international conference on Machine learning, 233-240, 2006 | 8297 | 2006 |
| Learning from positive and unlabeled data: A survey J Bekker, J Davis Machine Learning 109 (4), 719-760, 2020 | 734 | 2020 |
| Actions speak louder than goals: Valuing player actions in soccer T Decroos, L Bransen, J Van Haaren, J Davis Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 336 | 2019 |
| Deep transfer via second-order markov logic J Davis, P Domingos Proceedings of the 26th annual international conference on machine learning …, 2009 | 295 | 2009 |
| Learning first-order horn clauses from web text S Schoenmackers, J Davis, O Etzioni, DS Weld Proceedings of the 2010 conference on empirical methods in natural language …, 2010 | 294 | 2010 |
| Lifted probabilistic inference by first-order knowledge compilation G Van den Broeck, N Taghipour, W Meert, J Davis, L De Raedt IJCAI, 2178-2185, 2011 | 240 | 2011 |
| Estimating the class prior in positive and unlabeled data through decision tree induction J Bekker, J Davis Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 148 | 2018 |
| Unachievable region in precision-recall space and its effect on empirical evaluation K Boyd, VS Costa, J Davis, CD Page Proceedings of the... International Conference on Machine Learning …, 2012 | 132 | 2012 |
| Machine learning with a reject option: A survey K Hendrickx, L Perini, D Van der Plas, W Meert, J Davis Machine Learning 113 (5), 3073-3110, 2024 | 131 | 2024 |
| Beyond the selected completely at random assumption for learning from positive and unlabeled data J Bekker, P Robberechts, J Davis Joint European conference on machine learning and knowledge discovery in …, 2019 | 122 | 2019 |
| Semi-Supervised Anomaly Detection with an Application to Water Analytics. V Vercruyssen, W Meert, G Verbruggen, K Maes, R Baumer, J Davis ICDM 2018, 527-536, 2018 | 112 | 2018 |
| Automatic discovery of tactics in spatio-temporal soccer match data T Decroos, J Van Haaren, J Davis Proceedings of the 24th acm sigkdd international conference on knowledge …, 2018 | 111 | 2018 |
| Learning Markov network structure with decision trees D Lowd, J Davis 2010 IEEE International Conference on Data Mining, 334-343, 2010 | 109 | 2010 |
| Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings1 ES Burnside, J Davis, J Chhatwal, O Alagoz, MJ Lindstrom, BM Geller, ... Radiology 251 (3), 663-672, 2009 | 106 | 2009 |
| Markov network structure learning: A randomized feature generation approach J Van Haaren, J Davis Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1148-1154, 2012 | 102 | 2012 |
| Relationships between the external and internal training load in professional soccer: what can we learn from machine learning? A Jaspers, TO De Beéck, MS Brink, WGP Frencken, F Staes, JJ Davis, ... International journal of sports physiology and performance 13 (5), 625-630, 2018 | 90 | 2018 |
| View Learning for Statistical Relational Learning: With an Application to Mammography. J Davis, ES Burnside, I de Castro Dutra, D Page, R Ramakrishnan, ... IJCAI, 677-683, 2005 | 87 | 2005 |
| Lifted variable elimination: Decoupling the operators from the constraint language N Taghipour, D Fierens, J Davis, H Blockeel Journal of Artificial Intelligence Research 47, 393-439, 2013 | 84 | 2013 |
| An integrated approach to learning Bayesian networks of rules J Davis, E Burnside, I de Castro Dutra, D Page, VS Costa Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 2005 | 82 | 2005 |
| Predicting soccer highlights from spatio-temporal match event streams T Decroos, V Dzyuba, J Van Haaren, J Davis Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 77 | 2017 |
Wannes MeertDTAI, KU LeuvenVerified email at cs.kuleuven.be
