What are some applications of SVM?

Data Science, AI and ML
#1

Following are some of the applications of SVM:

  • Face detection – SVMs classify parts of the image as a face and non-face and create a square boundary around the face.
  • Text and hypertext categorization – SVMs allow Text and hypertext categorization for both inductive and transudative models. They use training data to classify documents into different categories. It categorizes on the basis of the score generated and then compares with the threshold value.
  • Classification of images – Use of SVMs provides better search accuracy for image classification. It provides better accuracy in comparison to the traditional query-based searching techniques.
  • Bioinformatics – It includes protein classification and cancer classification. We use SVM for identifying the classification of genes, patients on the basis of genes and other biological problems.
  • Protein fold and remote homology detection – Apply SVM algorithms for protein remote homology detection.
  • Handwriting recognition – We use SVMs to recognize handwritten characters used widely.
  • Generalized predictive control(GPC) – Use SVM based GPC to control chaotic dynamics with useful parameters.