首页|Findings on Boltzmann Machines Detailed by Investigators at Ministry of Natural Resources (Lithofacies Prediction Driven By Logging-based Bayesian-optimized Ens emble Learning: a Case Study of Lacustrine Carbonate Reservoirs)
Findings on Boltzmann Machines Detailed by Investigators at Ministry of Natural Resources (Lithofacies Prediction Driven By Logging-based Bayesian-optimized Ens emble Learning: a Case Study of Lacustrine Carbonate Reservoirs)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Boltzmann Mac hines have been published. According to news reporting from Beijing, People's Re public of China, by NewsRx journalists, research stated, "Although lithofacies r outinely is featured by distinct logging responses from each other, many types o f lithofacies in practical cases show similar measuring characteristics on logs, and then to achieve a desirable solution from logging-based lithofacies predict ion actually is challengeable. Since the mathematical essence of lithofacies pre diction can be explained as an issue of pattern recognition, a light gradient bo osting machine, a stateof- the-art ensemble learning, specifically developed to address supervised classification, could be a potential solver."
BeijingPeople's Republic of ChinaAsiaBoltzmann MachinesAlkaliesAnionsBoltzmann MachineCarbonatesCarbonic AcidEmerging TechnologiesMachine LearningMinistry of Natural Resources
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