Robotics & Machine Learning Daily News2024,Issue(Nov.7) :53-54.

Studies Conducted at Jilin University on Machine Learning Recently Reported (Com bining Categorical Boosting and Shapley Additive Explanations for Building an In terpretable Ensemble Classifier for Identifying Mineralization-related Geochemic al ...)

Robotics & Machine Learning Daily News2024,Issue(Nov.7) :53-54.

Studies Conducted at Jilin University on Machine Learning Recently Reported (Com bining Categorical Boosting and Shapley Additive Explanations for Building an In terpretable Ensemble Classifier for Identifying Mineralization-related Geochemic al ...)

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Abstract

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 from Jilin, People’s Republic of C hina, by NewsRx journalists, research stated, “The vast majority of shallow and deep learning techniques used to identify mineralization-related geochemical ano malies are black-box algorithms that lack the ability to elucidate the individua l contributions of each element towards the model predictions. In addition, most of the anomaly identification models established by both shallow and deep learn ing algorithms lack robustness.”

Key words

Jilin/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Jilin University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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