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Found 9 papers, 1 papers with code
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility.
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation
The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods.
Boosting the Learning for Ranking Patterns
On the other hand, several measures are often used to evaluate the interestingness of patterns, with the hope to reveal a ranking that is as close as possible to the user-specific ranking.
Solve Optimization Problems with Unknown Constraint Networks
In this paper, we propose Learn&Optimize, a method to solve optimization problems with known objective function and unknown constraint network.
Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach
The problem of discovering frequent itemsets including rare ones has received a great deal of attention.
Computational Complexity of Three Central Problems in Itemset Mining
Itemset mining is one of the most studied tasks in knowledge discovery.
Partial Queries for Constraint Acquisition
Learning constraint networks is known to require a number of membership queries exponential in the number of variables.
Users Constraints in Itemset Mining
Discovering significant itemsets is one of the fundamental problems in data mining.
A global constraint for closed itemset mining
Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining.
