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TWSVM for Unsupervised and Semi-supervised Learning

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Twin Support Vector Machines

Part of the book series: Studies in Computational Intelligence ((SCI,volume 659))

Abstract

Semi-supervised learning falls between unsupervised learning (with no labeled information of training data) and supervised learning (with completely labeled training data). Recent researches in machine-learning has shown that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. This chapter discusses twin support vector machine based algorithms for unsupervised and semi-supervised framework.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (IIT), New Delhi, India

    Jayadeva

  2. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University (SAU), New Delhi, India

    Reshma Khemchandani

  3. Department of Mathematics, Indian Institute of Technology (IIT), New Delhi, India

    Suresh Chandra

Authors
  1. Jayadeva
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  2. Reshma Khemchandani
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  3. Suresh Chandra
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Correspondence to Reshma Khemchandani .

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Jayadeva, Khemchandani, R., Chandra, S. (2017). TWSVM for Unsupervised and Semi-supervised Learning. In: Twin Support Vector Machines. Studies in Computational Intelligence, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-46186-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-46186-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46184-7

  • Online ISBN: 978-3-319-46186-1

  • eBook Packages: EngineeringEngineering (R0)

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