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Supervised and Semi-supervised Multi-task Binary Classification

  • Rakesh Kumar SanodiyaEmail author
  • Sriparna Saha
  • Jimson Mathew
  • Arpita Raj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

In this paper, we interrogate multi-task learning in the background of Gaussian Processes(GP) for constructing different models dealing with the issue of binary classification. At first, we propose a new supervised multi-task classification approach (SMBGC) based on Gaussian processes where kernel parameters for all tasks share a common prior. In recent years great advancement in the field of machine learning domain is being done by exploitation and extraction of information from unlabeled data. Machine learning models require labeled data for training but the amount of labeled data available is quite low since labeling them is expensive. To overcome this problem we came up with a semi-supervised multi-task binary Gaussian process classification (SSMBGC). In this approach, even small amount of labeled data can contribute to our model training and hence they enhance the generalization performance of a model on a learning task with the help of some other related tasks.

Keywords

Supervised Semi-supervised Gaussian process Classification 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rakesh Kumar Sanodiya
    • 1
    Email author
  • Sriparna Saha
    • 1
  • Jimson Mathew
    • 1
  • Arpita Raj
    • 2
  1. 1.Indian Institute of Technology PatnaPatnaIndia
  2. 2.Indian Institute of Engineering Science and TechnologyShibpurIndia

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