首页|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 ...)
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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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.”
JilinPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningJilin University
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