A Survey on Semi-Supervised Learning Techniques
It provides an overview for researchers, but is incremental as it summarizes existing techniques without novel contributions.
This paper surveys key approaches in semi-supervised learning, which tackles the problem of learning from both labeled and unlabeled data to reduce human labor and improve accuracy, but does not present new results or concrete numbers.
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semisupervised learning. In this paper we bring out some of the key approaches for semisupervised learning.