Real-Life Applications of SVM (Support Vector Machines)

Applications of SVM in Real World
As we have seen, 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 hypertext 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 handwritten 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.
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