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 …

The most important asset of semisupervised classification methods is the use of available
unlabeled data combined with a clearly smaller set of labeled examples, so as to increase …

This article presents a novel 'self-training'based semi-supervised classification algorithm
using the property of aggregation pheromone found in real ants. The proposed method has …

Semi-supervised learning aims at training accurate prediction models on labeled and
unlabeled data. Its realization strongly depends on selecting pseudo-labeled data. The …

Labeled data is the main ingredient for classification tasks. Labeled data is not always
available and free. Semi-supervised learning solves the problem of labeling the unlabeled …

Semi-supervised classifiers combine labeled and unlabeled data during the learning phase
in order to increase classifier's generalization capability. However, most successful semi …

Semi-supervised learning methods create models from a few labeled instances and a great
number of unlabeled instances. They appear as a good option in scenarios where there is a …

In this paper we present a novel template update algorithm based on the semi-supervised
learning algorithm Aggregation Pheromone Density Based Semi-Supervised Classification …

Semi-supervised algorithms are well-known for their ability to combine both supervised and
unsupervised strategies for optimizing their learning ability under the assumption that only a …

This Paper represents a literature review of Swarm intelligence algorithm in the area of semi-
supervised classification. There are many research papers for applying swarm intelligence …