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A Survey on Supervised and Unsupervised Learning Techniques

  • K. Sindhu Meena
  • S. Suriya
Conference paper
  • 37 Downloads

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.

Keywords

Supervised learning Unsupervised learning Support vector machine Decision tree Naïve Bayes KNN Linear regression K-means Agglomerative divisive Neural networks 

Abbreviation

KNN

K-nearest neighbor

WSD

Word-sense disambiguation

CNN

Convolution neural network

DT

Decision tree

NB

Naïve Bayes

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • K. Sindhu Meena
    • 1
  • S. Suriya
    • 1
  1. 1.Department of Computer Science and EngineeringPSG College of TechnologyCoimbatoreIndia

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