Abstract Machine learning has become the state-of-the-art technique for many tasks
including computer vision, natural language processing, speech processing tasks, etc …

This study analyses the main challenges, trends, technological approaches, and artificial
intelligence methods developed by new researchers and professionals in the field of …

Multi-view semi-supervised classification is inherently a challenging task in multi-view
learning due to the lack of label information. Existing methods generally suffer from …

Multimodal regression is a fundamental task, which integrates the information from different
sources to improve the performance of follow-up applications. However, existing methods …

Distinguishing the importance of views plays a key role in multi-view learning as each view
often contributes differently to a specific task. However, existing strategies generally attach …

Objective. To take full advantage of both labeled data and unlabeled ones, the Graph
Convolutional Network (GCN) was introduced in electroencephalography (EEG) based …

Multi-view semi-supervised feature selection can identify a feature subset from
heterogeneous feature spaces of data. However, existing methods fail in handling large …

Research in semi-supervised learning on graphs has attracted more and more attention in
recent years, as learning on graphs is applied in more and more domains and labeling data …

Semi-supervised classification aims to leverage a small amount of labeled data for learning
tasks. Multi-view semi-supervised classification has attracted widespread attention because …

Deep learning has advanced the automation and intelligence levels of medical image
segmentation, but the acquisition of annotations for medical images proves to be very …