首页|Study Results from Deakin University Update Understanding of Machine Learning (M apping Surface Sediment Characteristics In Enclosed Shallow-marine Environments Using Spatially Balanced Designs and the Random Forest Algorithm)
Study Results from Deakin University Update Understanding of Machine Learning (M apping Surface Sediment Characteristics In Enclosed Shallow-marine Environments Using Spatially Balanced Designs and the Random Forest Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting out of Warrnambool, Australia, by NewsRx editor s, research stated, “Mapping the sedimentary character of the seafloor in large water-filled basins is fundamental for understanding landform dynamics to inform research, management, intervention and conservation actions. Seabed mapping met hods have undergone considerable development in the last two decades, including the uptake of machine learning approaches for sediment size prediction and class ification.” Financial supporters for this research include Port Phillip Bay Beach Renourishm ent Program, Deakin University, as part of the Wiley - Deakin University agreeme nt via the Council of Australian University Librarians.
WarrnamboolAustraliaAustralia and Ne w ZealandAlgorithmsCyborgsEmerging TechnologiesMachine LearningDeakin University
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