Jesse Davis
Jesse Davis
Professor, Department of Computer Science, KU Leuven
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The relationship between Precision-Recall and ROC curves
J Davis, M Goadrich
Proceedings of the 23rd international conference on Machine learning, 233-240, 2006
Learning from positive and unlabeled data: A survey
J Bekker, J Davis
Machine Learning 109 (4), 719-760, 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
Deep transfer via second-order markov logic
J Davis, P Domingos
Proceedings of the 26th annual international conference on machine learning …, 2009
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
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
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
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
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
Learning Markov network structure with decision trees
D Lowd, J Davis
2010 IEEE International Conference on Data Mining, 334-343, 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
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
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
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
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
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
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
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
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
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
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