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1 Ergebnisse
1
Improving SAT-solving with Machine Learning:
, In:
Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
,
Wu, Haoze
- p. 787-788 , 2017
Link:
https://dl.acm.org/doi/10.1145/3017680.3022464
RT T1 <cTitle>Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education</cTitle>: T1 <Title>Improving SAT-solving with Machine Learning</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3022464&Exemplar=1&LAN=DE A1 Wu, Haoze PB ACM YR 2017 K1 3SAT K1 logistic K1 machine learning K1 monte-carlo K1 satisfiability K1 Hardware K1 Hardware validation K1 Functional verification K1 Theorem proving and SAT solving K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Supervised learning K1 Supervised learning by classification SP 787 OP 788 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3017680.3022464 DO https://dl.acm.org/doi/10.1145/3017680.3022464 SF ELIB - SuUB Bremen
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