We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

Land-cover map** in remote sensing (RS) applications renders rich information for
decision support and environmental monitoring systems. The derivation of such information …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Due to the efficiency of learning relationships and complex structures hidden in data, graph-
oriented methods have been widely investigated and achieve promising performance …

In recent years, a great many methods of learning from multi-view data by considering the
diversity of different views have been proposed. These views may be obtained from multiple …

We consider the problem of function estimation in the case where an underlying causal
model can be inferred. This has implications for popular scenarios such as covariate shift …

On-device learning promises collaborative training of machine learning models across edge
devices without the sharing of user data. In state-of-the-art on-device learning algorithms …

Biological systems function through complex interactions between various 'omics
(biomolecules), and a more complete understanding of these systems is only possible …

Abstract Nowadays, Semi-Supervised Learning lies at the core of the Machine Learning field
trying to effectively exploit unlabeled data as much as possible, together with a small amount …

Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the
Earth using advanced imaging instruments which can produce high-dimensional images …