A comprehensive survey on support vector machine classification: Applications, challenges and trends

By Jair Cervantes, Farid García, L. Rodríguez-Mazahua, Asdrúbal López-Chau. Neurocomputing, 2020

2,029 citations recorded by the source corpus.

Abstract In recent years, an enormous amount of research has been carried out on support vector machines (SVMs) and their application in several fields of science. SVMs are one of the most powerful and robust classification and regression algorithms in multiple fields of application. The SVM has been playing a significant role in pattern recognition which is an extensively popular and active research area among the researchers. Research in some fields where SVMs do not perform well has spurred development of other applications such as SVM for large data sets, SVM for multi classification and SVM for unbalanced data sets. Further, SVM has been integrated with other advanced methods such as evolve algorithms, to enhance the ability of classification and optimize parameters. SVM algorithms have gained recognition in research and applications in several scientific and engineering areas. This paper provides a brief introduction of SVMs, describes many applications and summarizes challenges and trends. Furthermore, limitations of SVMs will be identified. The future of SVMs will be discussed in conjunction with further applications. The applications of SVMs will be reviewed as well, especially in the some fields.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/88053e5c116dd74debacbab03de076847669371c

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In Artificial Intelligence

  1. Joaquim R. Martins
  2. Ji-chao Li
  3. Xiaosong Du
  4. Gaetan K. W. Kenway

In Computer Vision and Pattern Recognition

  1. Joaquim R. Martins
  2. Ji-chao Li
  3. Xiaosong Du

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