Semi-supervised learning: a brief review

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    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.

     


  • Keywords


    Semi-Supervised Learning; Labeled Data; Unlabeled Data; SSL Methods; Training Data; Test Data.

  • References


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Article ID: 9977
 
DOI: 10.14419/ijet.v7i1.8.9977




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