Abstract—In a series of papers by Dai and colleagues [1], [2], a feature map (or kernel) was introduced for semiand unsupervised learning. This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels. In this critique, we analyze the latest version of this series of papers, which is called Ensemble Projections [2]. We show that the ...

Semi-supervised category learning is when participants make classification judgements while receiving feedback about the right answers on some trials (labeled stimuli) but not others (unlabeled stimuli). Sporadic feedback is common outside the laboratory, and it is important to understand how people learn in this setting. While there are numerous recent studies, the strength and robustness of s...

“The statistical problem of testing cluster validity is essentially unsolved” [5]. We translate the issue of gaining credibility on the output of un-supervised learning algorithms to the supervised learning case. We introduce a notion of instance easiness to supervised learning and link the validity of a clustering to how its output constitutes an easy instance for supervised learning. Our noti...

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...

Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, util...

Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ...

A major challenge in pattern recognition is labeling of large numbers of samples. This problem has been solved by extending supervised learning to semi-supervised learning. Thus semi-supervised learning has become one of the most important methods on the research of facial expression recognition. Frontal and un-occluded face images have been well recognized using traditional facial expression r...

In this paper we investigate techniques for semi-supervised learning that split their unsupervised and supervised components — that is, an initial unsupervised phase is followed by a supervised learning phase. We first analyze the relative value of labeled and unlabeled data. We then present methods that perform “split” semi-supervised learning and show promising empirical results.

Unlabelled examples in supervised learning tasks can be optimally exploited using semi-supervised methods and active learning. We focus on ranking learning from pairwise instance preference to discuss these important extensions, semi-supervised learning and active learning, in the probabilistic framework of Gaussian processes. Numerical experiments demonstrate the capacities of these techniques.