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A Survey on Supervised and Unsupervised Learning Techniques

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Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications (AISGSC 2019 2019)

Abstract

Supervised learning is the popular version of machine learning. It trains the system in the training phase by labeling each of its input with its desired output value. Unsupervised learning is another popular version of machine learning which generates inferences without the concept of labels. The most common supervised learning methods are linear regression, support vector machine, random forest, naïve Bayes, etc. The most common unsupervised learning methods are cluster analysis, K-means, Apriori algorithm, etc. This survey paper gives an overview of supervised algorithms, namely, support vector machine, decision tree, naïve Bayes, KNN, and linear regression, and an overview of unsupervised algorithms, namely, K-means, agglomerative divisive, and neural networks.

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Abbreviations

KNN:

K-nearest neighbor

WSD:

Word-sense disambiguation

CNN:

Convolution neural network

DT:

Decision tree

NB:

Naïve Bayes

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    K. Sindhu Meena & S. Suriya

Authors
  1. K. Sindhu Meena
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  2. S. Suriya
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Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    L. Ashok Kumar

  2. Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    L. S. Jayashree

  3. Department of Information Technology, PSG College of Technology, Coimbatore, Tamil Nadu, India

    R. Manimegalai

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Sindhu Meena, K., Suriya, S. (2020). A Survey on Supervised and Unsupervised Learning Techniques. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_58

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  • DOI: https://doi.org/10.1007/978-3-030-24051-6_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24050-9

  • Online ISBN: 978-3-030-24051-6

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