Perspectives on simulation in education and training: virtual environments for the training of maintenance and service tasks

Multi-label learning aims at assigning a set of appropriate labels to multi-label samples. Although it has been successfully applied in various domains in recent years, most multi-label learning methods require sufficient labeled training samples, ...

In many practical data mining applications, such as Web page classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as co-training have ...

Practical machine learning and data mining problems often face shortage of labeled training data. Self-training algorithms are among the earliest attempts of using unlabeled data to enhance learning. Traditional self-training algorithms label unlabeled ...