Abstract Machine learning systems must adapt to data distributions that evolve over time, in
applications ranging from sensor networks and self-driving car perception modules to brain …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

This book presents the features and advantages offered by complex networks in the
machine learning domain. In the first part, an overview on complex networks and network …

In unsupervised domain adaptation, existing theory focuses on situations where the source
and target domains are close. In practice, conditional entropy minimization and pseudo …

In this paper, we propose a probabilistic framework for analyzing a multi-class majority vote
classifier in the case where training data is partially labeled. First, we derive a multi-class …

Domain adaptation (DA) arises as an important problem in statistical machine learning when
the source data used to train a model is different from the target data used to test the model …

Clustering with partial supervision finds its application in situations where data is neither
entirely nor accurately labeled. This paper discusses a semi-supervised clustering algorithm …

Transductive support vector machines (TSVM) has been widely used as a means of treating
partially labeled data in semisupervised learning. Around it, there has been mystery …

In classification, semi-supervised learning occurs when a large amount of unlabeled data is
available with only a small number of labeled data. In such a situation, how to enhance …

In a scenario of supervised classification of data, labeled training data is essential.
Unfortunately, the process by which those labels are obtained is not error-free, for example …