PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
MIT Press, Cambridge, MA, USA (1994)
2 Pith papers cite this work, alongside 743 external citations. Polarity classification is still indexing.
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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.
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On the Limits of PAC Learning of Networks from Opinion Dynamics
PAC learning of networks from threshold opinion dynamics is efficient when influencers per agent are bounded but computationally hard for majority rules, with a heuristic succeeding in over 98% of simulations.
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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.