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.
Neural Computation 9(8), 1735–1780 (1997)
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
AI's compositional reasoning failures originate in psychological learning paradigms that shaped its architectures, and the ReSynth trimodular framework is proposed to embed systematicity structurally.
Combines automata learning and model-based testing to generate training data for recurrent neural networks modeling hybrid systems, yielding fivefold lower crash-detection error on a platooning scenario with up to 1000x fewer samples than random data.
citing papers explorer
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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.
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How Psychological Learning Paradigms Shaped and Constrained Artificial Intelligence
AI's compositional reasoning failures originate in psychological learning paradigms that shaped its architectures, and the ReSynth trimodular framework is proposed to embed systematicity structurally.
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Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
Combines automata learning and model-based testing to generate training data for recurrent neural networks modeling hybrid systems, yielding fivefold lower crash-detection error on a platooning scenario with up to 1000x fewer samples than random data.