Domain adaptation transfer learning soft sensor for product quality prediction
For multi-grade chemical processes, often, limited labeled data are available, resulting in an
insufficient construction of reliable soft sensors for several modes. Additionally, the current …
insufficient construction of reliable soft sensors for several modes. Additionally, the current …
Transfer of semi-supervised broad learning system in electroencephalography signal classification
Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–
computer interface (BCI) system. Traditional supervised learning methods have performed …
computer interface (BCI) system. Traditional supervised learning methods have performed …
Safe semi-supervised learning for pattern classification
J Ma, G Yu, W Xiong, X Zhu - Engineering Applications of Artificial …, 2023 - Elsevier
Semi-supervised learning (SSL) based on manifold regularization in many fields has
attracted widespread attention and research. However, SSL still has two main challenges …
attracted widespread attention and research. However, SSL still has two main challenges …
Safety-aware graph-based semi-supervised learning
In machine learning field, Graph-based Semi-Supervised Learning (GSSL) has recently
attracted much attention and many researchers have proposed a number of different …
attracted much attention and many researchers have proposed a number of different …
Confidence-weighted safe semi-supervised clustering
In this paper, we propose confidence-weighted safe semi-supervised clustering where prior
knowledge is given in the form of class labels. In some applications, some samples may be …
knowledge is given in the form of class labels. In some applications, some samples may be …
Safe semi-supervised extreme learning machine for EEG signal classification
One major challenge in the current brain–computer interface research is the accurate
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …
Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine
Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …
Dual learning-based safe semi-supervised learning
H Gan, Z Li, Y Fan, Z Luo - IEEE Access, 2017 - ieeexplore.ieee.org
In many real-world applications, labeled instances are generally limited and expensively
collected, while the most instances are unlabeled and the amount is often sufficient …
collected, while the most instances are unlabeled and the amount is often sufficient …
Adaptive safe semi-supervised extreme machine learning
J Ma, C Yuan - IEEE Access, 2019 - ieeexplore.ieee.org
Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent
learning framework. However, the performance of SSL heavily depends on the construction …
learning framework. However, the performance of SSL heavily depends on the construction …
SSDBA: the stretch shrink distance based algorithm for link prediction in social networks
In the field of social network analysis, Link Prediction is one of the hottest topics which has
been attracted attentions in academia and industry. So far, literatures for solving link …
been attracted attentions in academia and industry. So far, literatures for solving link …