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Real-Life Applications of Support Vector Machines

Applications of SVM in Real World 

SVMs depends on supervised learning algorithms. The aim of using SVM is to correctly classify unseen data. SVMs have a number of applications in several fields.

Some common applications of SVM are-

  • Face detection – SVMc 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 hyper text categorization for both inductive and transductive 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 hand written characters used widely.
  • Generalized predictive control(GPC) – Use SVM based GPC to control chaotic dynamics with useful parameters.

Let us now see the above applications of SVM in detail:

1. Face Detection

It classifies the parts of the image as face and non-face. It contains training data of n x n pixels with a two-class face (+1) and non-face (-1). Then it extracts features from each pixel as face or non-face. Creates a square boundary around faces on the basis of pixel brightness and classifies each image by using the same process.

2. Text and Hypertext Categorization

Allows text and hypertext categorization for both types of models; inductive and transductive. It Uses training data to classify documents into different categories such as news articles, e-mails, and web pages


  • Classification of news articles into “business” and “Movies”
  • Classification of web pages into personal home pages and others

For each document, calculate a score and compare it with a predefined threshold value. When the score of a document surpasses threshold value, then the document is classified into a definite category. If it does not surpass threshold value then consider it as a general document.

Classify new instances by computing score for each document and comparing it with learned threshold.

3. Classification of Images

SVMs can classify images with higher search accuracy. Its accuracy is higher than traditional query based refinement schemes

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