Semi-supervised learning: a brief review

  • Authors

    • Y C A Padmanabha Reddy
    • P Viswanath
    • B Eswara Reddy
    2018-02-09
    https://doi.org/10.14419/ijet.v7i1.8.9977
  • Semi-Supervised Learning, Labeled Data, Unlabeled Data, SSL Methods, Training Data, Test Data.

  • 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.

     

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  • How to Cite

    C A Padmanabha Reddy, Y., Viswanath, P., & Eswara Reddy, B. (2018). Semi-supervised learning: a brief review. International Journal of Engineering & Technology, 7(1.8), 81-85. https://doi.org/10.14419/ijet.v7i1.8.9977

    Received date: 2018-03-08

    Accepted date: 2018-03-08

    Published date: 2018-02-09