首页|New Machine Learning Findings from Peking University Health Sciences Center Desc ribed (The Neat Equating Via Chaining Random Forests In the Context of Small Sam ple Sizes: a Machine-learning Method)
New Machine Learning Findings from Peking University Health Sciences Center Desc ribed (The Neat Equating Via Chaining Random Forests In the Context of Small Sam ple Sizes: a Machine-learning Method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news originating from Beijing, People’s Republic o f China, by NewsRx correspondents, research stated, “The part of responses that is absent in the nonequivalent groups with anchor test (NEAT) design can be mana ged to a planned missing scenario.” Funders for this research include National Natural Science Foundation of China ( NSFC), Peking University. Our news journalists obtained a quote from the research from Peking University H ealth Sciences Center, “In the context of small sample sizes, we present a machi ne learning (ML)-based imputation technique called chaining random forests (CRF) to perform equating tasks within the NEAT design. Specifically, seven CRF-based imputation equating methods are proposed based on different data augmentation m ethods.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningPeking University Health Sc iences Center
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