Robotics & Machine Learning Daily News2024,Issue(Oct.14) :33-34.

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)

Robotics & Machine Learning Daily News2024,Issue(Oct.14) :33-34.

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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Abstract

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.”

Key words

Beijing/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Peking University Health Sc iences Center

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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