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 …

Self-labeled techniques are semi-supervised classification methods that address the
shortage of labeled examples via a self-learning process based on supervised models. They …

There has been increased interest in devising learning techniques that combine unlabeled
data with labeled data? ie semi-supervised learning. However, to the best of our knowledge …

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 …

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 …

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 …

There has been an increase of interest for semi-supervised learning recently, because of the
many datasets with large amounts of unlabeled examples and only a few labeled ones. This …

Semisupervised learning is a learning standard which deals with the study of how
computers and natural systems such as human beings acquire knowledge in the presence …

Most of the semi-supervised classification methods developed so far use unlabeled data for
regularization purposes under particular distributional assumptions such as the cluster …

Semi-supervised learning has recently emerged as a new paradigm in the machine learning
community. It aims at exploiting simultaneously labeled and unlabeled data for classification …