Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …

Semi-supervised classification methods are suitable tools to tackle training sets with large
amounts of unlabeled data and a small quantity of labeled data. This problem has been …

Machine-learning (ML) enables computers to learn how to recognise patterns, make
unintended decisions, or react to a dynamic environment. The effectiveness of trained …

Co‐training algorithm is one of the main methods of semi‐supervised learning in machine
learning, which explores the effective information in unlabeled data by multi‐learner …

High resolution remote sensing (HRRS) image scene classification plays a crucial role in a
wide range of applications and has been receiving significant attention. Recently …

In many real-world tasks, there are abundant unlabeled examples but the number of labeled
training examples is limited, because labeling the examples requires human efforts and …

Modern data-driven fault detection and diagnosis (FDD) techniques show impressive high
diagnostic accuracy in recognizing various air handling units (AHUs) faults. Most existing …