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 …

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 …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

Support vector machine (SVM) is a machine learning method based on statistical learning
theory. It has a lot of advantages, such as solid theoretical foundation, global optimization …

Educational data mining constitutes a recent research field which gained popularity over the
last decade because of its ability to monitor students' academic performance and predict …

Crowdsourcing systems provide a cost effective and convenient way to collect labels, but
they often fail to guarantee the quality of the labels. This paper proposes a novel framework …

Semisupervised learning (SSL) methods have been proved to be effective at solving the
labeled samples shortage problem by using a large number of unlabeled samples together …

We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …

Semi-supervised data are quite common in the process industry, which has caught much
attention in recent years. The semi-supervised feature of process data not only has a great …