Semi-supervised learning (SSL) based on manifold regularization in many fields has
attracted widespread attention and research. However, SSL still has two main challenges …

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

Semi-supervised classification methods attempt to improve classification performance based
on a small amount of labeled data through full use of abundant unlabeled data. Although …

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 …

Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent
learning framework. However, the performance of SSL heavily depends on the construction …

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 …

Face recognition (FR) is a fundamental problem in a biometric identification system and has
attracted much attention in pattern recognition and computer vision fields. Since human face …

Ensemble learning is an effective technique to learn the information of data by combining
multiple models. But usually the combined models are supervised learning algorithms which …

In practical application, the data are imbalanced, it is difficult to find the balanced, rather
skewed data is the common occurrence. This poses a severe challenge to the classification …