59 citations · 66 across the 3 of their papers we have counts for
5 papers
Induction of Interpretable Possibilistic Logic Theories from Relational Data
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kin…
Learning Possibilistic Logic Theories from Default Rules
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta". We first analyse this problem from the point of view of m…
Encoding Markov Logic Networks in Possibilistic Logic
Ondrej Kuzelka, Jesse Davis, Steven Schockaert
Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be di…
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
Kendrick Boyd, Vitor Santos Costa, Jesse Davis +1
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used ana…
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Jesse Davis, Vitor Santos Costa, Peggy Peissig +3
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statist…