An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.
In: 2017 IEEE International Conference on Software Maintenance and Evo- lution, ICSME 2017, Shanghai, China, September 17-22
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
N-gram models equipped with a dynamic promotion ensemble match or exceed the accuracy of neural networks for next-activity prediction in event logs while using substantially fewer resources.
citing papers explorer
-
An $L^{\#}$ Based Algorithm for Active Learning of Minimal Separating Automata
An L#-inspired active learning algorithm learns minimal separating DFAs for disjoint languages when one exists and outperforms prior methods on random and industrial benchmarks.
-
Promoting Simple Agents: Ensemble Methods for Event-Log Prediction
N-gram models equipped with a dynamic promotion ensemble match or exceed the accuracy of neural networks for next-activity prediction in event logs while using substantially fewer resources.