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2020 | OriginalPaper | Chapter

58. A Survey on Supervised and Unsupervised Learning Techniques

Authors : K. Sindhu Meena, S. Suriya

Published in: Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications

Publisher: Springer International Publishing

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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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Metadata
Title
A Survey on Supervised and Unsupervised Learning Techniques
Authors
K. Sindhu Meena
S. Suriya
Copyright Year
2020
Book
Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications

Print ISBN: 978-3-030-24050-9

Electronic ISBN: 978-3-030-24051-6

Copyright Year: 2020

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