Data on Machine Learning Reported by Researchers at School of Resources & Safety Engineering (Uniaxial Compressive Strength Prediction for Rock Material I n Deep Mine Using Boosting-based Machine Learning Methods and Optimization Algor ithms)
Data on Machine Learning Reported by Researchers at School of Resources & Safety Engineering (Uniaxial Compressive Strength Prediction for Rock Material I n Deep Mine Using Boosting-based Machine Learning Methods and Optimization Algor ithms)
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in Machine Lea rning. According to news originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Traditional laboratory tests for mea suring rock uniaxial compressive strength (UCS) are tedious and timeconsuming. T here is a pressing need for more effective methods to determine rock UCS, especi ally in deep mining environments under high in-situ stress.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
Key words
Changsha/People’s Republic of China/As ia/Algorithms/Cyborgs/Emerging Technologies/Machine Learning/Optimization A lgorithms/School of Resources & Safety Engineering
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