They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework.
Sep 3, 2019 · In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging ...
They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework.
They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework.
They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework.
In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy.
Xing He, Jun Ji, Kaixin Liu , Zengliang Gao , Yi Liu : Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models. Sensors 19(17): 3814 (2019).
Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models. Sensors 2019, 19, 3814. https://doi.org/10.3390/s19173814. AMA Style. He X, Ji J ...
May 14, 2021 · Basically, soft-sensing uses secondary variables (i.e., easy-to-measure variables) to estimate primary variables (i.e., hard-to-measure ...
A deep semi-supervised just-in-time learning-based Gaussian process regression (DSSJITGPR) is developed for Mooney viscosity estimation that shows ...
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