| The relationship between Precision-Recall and ROC curves J Davis, M Goadrich Proceedings of the 23rd international conference on Machine learning, 233-240, 2006 | 6278 | 2006 |
| Learning from positive and unlabeled data: A survey J Bekker, J Davis Machine Learning 109, 719-760, 2020 | 314 | 2020 |
| 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 | 264 | 2010 |
| Deep transfer via second-order markov logic J Davis, P Domingos Proceedings of the 26th annual international conference on machine learning …, 2009 | 261 | 2009 |
| Lifted probabilistic inference by first-order knowledge compilation G Van den Broeck, N Taghipour, W Meert, J Davis, L De Raedt Proceedings of the Twenty-Second international joint conference on …, 2011 | 204 | 2011 |
| 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 | 175 | 2019 |
| 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 | 102 | 2009 |
| 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 | 96 | 2018 |
| Unachievable region in precision-recall space and its effect on empirical evaluation K Boyd, VS Costa, J Davis, D Page arXiv preprint arXiv:1206.4667, 2012 | 94 | 2012 |
| Learning Markov network structure with decision trees D Lowd, J Davis 2010 IEEE International Conference on Data Mining, 334-343, 2010 | 94 | 2010 |
| Markov network structure learning: A randomized feature generation approach J Van Haaren, J Davis Twenty-sixth AAAI conference on artificial intelligence, 2012 | 88 | 2012 |
| 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 | 80 | 2005 |
| 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 | 80 | 2005 |
| Bottom-Up Learning of Markov Network Structure J Davis, PM Domingos Proceedings of the 27th International Conference on Machine Learning, 271-278, 2010 | 72 | 2010 |
| Semi-supervised anomaly detection with an application to water analytics V Vercruyssen, W Meert, G Verbruggen, K Maes, R Baumer, J Davis 2018 ieee international conference on data mining (icdm) 2018, 527-536, 2018 | 69 | 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 | 66 | 2018 |
| Automatically detecting and rating product aspects from textual customer reviews W Bancken, D Alfarone, J Davis Proceedings of the 1st international workshop on interactions between data …, 2014 | 65 | 2014 |
| 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 | 62 | 2013 |
| 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 | 60 | 2017 |
| Tractable learning for complex probability queries J Bekker, J Davis, A Choi, A Darwiche, G Van den Broeck Advances in Neural Information Processing Systems 28, 2015 | 59 | 2015 |
Jan Van HaarenClub Brugge and KU LeuvenVerified email at cs.kuleuven.be
