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 …

One major challenge in the current brain–computer interface research is the accurate
classification of time-varying electroencephalographic (EEG) signals. The labeled EEG …

In the past few years, Safe Semi-Supervised Learning (S3L) has become an emerging
research topic. A few studies have been investigated in the S3L field and obtained desired …

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 …

Abstract Recently, Safe Semi-Supervised Learning (S3L) has become an active topic in the
Semi-Supervised Learning (SSL) field. In S3L, unlabeled data that may affect the …

Estimation of black-box functions often requires evaluating an extensive number of
expensive noisy points. Learning algorithms can actively compare the similarity between the …

In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model
of GNN, has achieved promising results for non-Euclidean data by introducing convolution …

Within the past few years, Safe Semi-Supervised Learning (S3L) has become a hot topic in
the machine learning field and many related S3L methods have been proposed to safely …

Semi-supervised learning has attracted much attention in machine learning field over the
past decades and a number of algorithms are proposed to improve the performance by …

Recently, semi-supervised learning has received much attention in data mining and
machine learning, and a number of algorithms are proposed to discuss how to make good …