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-
Let us now see the above applications of SVM in detail:
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.
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
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.
SVMs can classify images with higher search accuracy. Its accuracy is higher than traditional query based refinement schemes