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Support vector machine (SVM) is a machine learning tool, which was initially proposed by Cortes and Vapnik in 1995 . It was originally developed to solve binary classification problems and has since been applied to a wide range of problems, including clustering, regression, data mining, pattern classification, and other classification problems. The main idea of SVM is to find a hyperplane that maximizes the margin between two classes. The hyperplane is the line separating the two classes. This is obtained by training a classifier by using training data points, and then using the support vectors to draw the hyperplane. The hyperplane is represented in a high dimensional space as a linear function. The number of support vectors is smaller than the number of training data points. The main idea of SVM is to find the best hyperplane to discriminate between the two classes. The hyperplane can be written as in Eq. 19.
The support vector machine (SVM) is a highly effective classification technique for solving binary classification problems. The main advantages of SVM are that it can be trained in a model-free manner, and it performs an optimum boundary for separating the two classes. Support vector machine was first developed by Cortes and Vapnik .
218. Yumer, T., Mahadevan, S., and Roy, C., “Deep Learning for Object Recognition in 2D Image Collections,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1471-1479, (2017).
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