Jesse Davis
Jesse Davis
Professor, Department of Computer Science, KU Leuven
Geverifieerd e-mailadres voor cs.kuleuven.be - Homepage
Geciteerd door
Geciteerd door
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, 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
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
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
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
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
Learning Markov network structure with decision trees
D Lowd, J Davis
2010 IEEE International Conference on Data Mining, 334-343, 2010
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
Markov network structure learning: A randomized feature generation approach
J Van Haaren, J Davis
Twenty-sixth AAAI conference on artificial intelligence, 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
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
Bottom-Up Learning of Markov Network Structure
J Davis, PM Domingos
Proceedings of the 27th International Conference on Machine Learning, 271-278, 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
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
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
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
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
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
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