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Classification of Machine Learning Algorithms

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Advances in Data Computing, Communication and Security

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 106))

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Abstract

Machine learning (ML) is to make logical patterns out of various types of input data including images, texts, numbers and any other types of data. Data derived from research will be processes through machine learning algorithms and leads to a prediction that is mainly considered as the output of the machine learning algorithm. In this paper, the most popular learning algorithms have been reviewed and their specific features are discussed to help select the most appropriate algorithm through comparison in different research projects. However, there is not just one efficient method to apply to all data sets, and the appropriate algorithm may differ based on factors in a study.

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Author information

Authors and Affiliations

  1. University Canada West, Vancouver, Canada

    Hamed Taherdoost

Authors
  1. Hamed Taherdoost
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Corresponding author

Correspondence to Hamed Taherdoost .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Pankaj Verma

  2. Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, India

    Chhagan Charan

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Xavier Fernando

  4. Department of Electrical and Computer Engineering, Oakland University, Rochester, MI, USA

    Subramaniam Ganesan

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Taherdoost, H. (2022). Classification of Machine Learning Algorithms. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_38

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