Semi-supervised learning: a brief review

Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this pro...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of engineering & technology (Dubai) Vol. 7; no. 1.8; p. 81
Main Authors C A Padmanabha Reddy, Y, Viswanath, P, Eswara Reddy, B
Format Journal Article
LanguageEnglish
Japanese
Published 09.02.2018
Online AccessGet full text

Cover

More Information
Summary:Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervised Clustering which achieves better accuracy than traditional supervised and unsupervised learning techniques. The paper also addresses the issue of scalability and applications of Semi-supervised learning. 
ISSN:2227-524X
2227-524X
DOI:10.14419/ijet.v7i1.8.9977