Supervised learning techniques construct predictive models by learning from a large
number of training examples, where each training example has a label indicating its ground …

A huge amount of data is generated daily leading to big data challenges. One of them is
related to text mining, especially text classification. To perform this task we usually need a …

This book addresses some theoretical aspects of semisupervised learning (SSL). The book
is organized as a collection of different contributions of authors who are experts on this topic …

There has been significant research into reject inference, with several statistical methods
and machine learning techniques having been employed to infer the possible repayment …

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 …

The paper categorizes and reviews the state-of-the-art approaches to the partially
supervised learning (PSL) task. Special emphasis is put on the fields of pattern recognition …

Semi-supervised learning constructs the predictive model by learning from a few labeled
training examples and a large pool of unlabeled ones. It has a wide range of application …

Motivation: Gene expression profiling has shown great potential in outcome prediction for
different types of cancers. Nevertheless, small sample size remains a bottleneck in obtaining …

Active learners alleviate the burden of labeling large amounts of data by detecting and
asking the user to label only the most informative examples in the domain. We focus here on …

This paper analyzes the performance of semisupervised learning of mixture models. We
show that unlabeled data can lead to an increase in classification error even in situations …