首页|Reports from Laurentian University Advance Knowledge in Machine Learning (Development and Application of Feature Engineered Geological Layers for Ranking Magmatic, Volcanogenic, and Orogenic System Components In Archean Greenstone Belts)
Reports from Laurentian University Advance Knowledge in Machine Learning (Development and Application of Feature Engineered Geological Layers for Ranking Magmatic, Volcanogenic, and Orogenic System Components In Archean Greenstone Belts)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating from Sudbury, Canada, by NewsRx correspondents, research stated, "Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning. Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques, which can lead to ambiguity, geological oversimplification, and/or compounding subjective bias." Funders for this research include Mushkegowuk (Cree) , Algonquin, Canada First Research Excellence Fund, Natural Sciences and Engineering Research Council of Canada (NSERC), Laurentian University's Mineral Exploration Research Center, LOOP at the University of Western Australia's Center for Exploration Targeting.
SudburyCanadaNorth and Central AmericaCyborgsEmerging TechnologiesEngineeringMachine LearningLaurentian University
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