This paper tackles the problem of semi-supervised learning when set labeled samples is limited to a small number images per class, typically less than 10, that we refer as barely-supervised learning. We analyze in depth behavior state-of-the-art method, FixMatch, which relies on weakly-augmented version an image obtain supervision signal for more strongly-augmented version. show it frequently f...

The development of robust classification model is among the important issues in computer vision. This paper deals with weakly supervised learning that generalizes the supervised and semi-supervised learning. In weakly supervised learning training data are given as the priors of each class for each sample. We first propose a weakly supervised strategy for learning soft decision trees. Besides, t...

In many real-world applications there are usually abundant unlabeled data but the amount of labeled training examples are often limited, since labeling the data requires extensive human effort and expertise. Thus, exploiting unlabeled data to help improve the learning performance has attracted significant attention. Major techniques for this purpose include semi-supervised learning and active l...

Semi-supervised learning and ensemble learning are two important machine learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong generalization by using multiple learners. Although both paradigms have achieved great success during the past decade, they were almost developed separately. In this paper, we advocat...

Studies of human category learning typically focus on situations where explicit category labels accompany each example (supervised learning) or on situations were people must infer category structure entirely from the distribution of unlabeled examples (unsupervised learning). However, real-world category learning likely involves a mixture of both types of learning (semi-supervised learning). S...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

Supervised learning aims to build a function or model that seeks as many mappings possible between the training data and outputs, where each will predict label match its corresponding ground-truth value. Although supervised has achieved great success in tasks, sufficient supervision for labels is not accessible domains because accurate labelling costly laborious, particularly medical image anal...

Automated short answer scoring is increasingly used to give students timely feedback about their learning progress. Building scoring models comes with high costs, as stateof-the-art methods using supervised learning require large amounts of hand-annotated data. We analyze the potential of recently proposed methods for semi-supervised learning based on clustering. We find that all examined metho...