Abstract
Support vector machine (SVM) is one of the most widely used classification algorithms. It uses supervised learning method (Aizerman et al., Auto Remote Cont 25:821–837, 1964) for training. The SVM classifier is mostly used in multi-classification problems. SVM differs from the traditional classifiers as it uses “decision boundary,” which separates the classes. The decision boundary maximizes distances of data points belongs to different classes .in this; decision boundary is the optimum that is Most optimal (Baron and Ensley, Opportunity recognition as the detection of meaningful patterns: evidence from the prototypes of novice and experienced entrepreneurs. Manuscript under review, 2005) decision boundary has maximum margin. The data points which are nearer to the boundary are called support vectors. The most important thing in SVM is its hyper plane, where for a N-dimensional space it is an (N-1)-dimensional subspace. To better understand, the hyper plane is just a line in one dimension for a two-dimensional space. It is a two-dimensional plane that separates the classes for a three-dimensional space.
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Bharadwaj, Prakash, K.B., Kanagachidambaresan, G.R. (2021). Pattern Recognition and Machine Learning. In: Prakash, K.B., Kanagachidambaresan, G.R. (eds) Programming with TensorFlow. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-57077-4_11
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