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IEEE/ICACT20240199 Slide.06        [Big Slide]	Oral Presentation
	SVM is particularly suited for binary classification, dealing with scenarios where there are only two classes. It relies on support vectors, which are subsets of training data close to the decision boundary. SVM uses the concept of a margin, defined as the distance between support vectors of the two classes separated by the decision boundary. The objective is to choose a decision boundary that maximizes the margin between the two classes. The goal of SVM is to achieve maximum margin classification, ensuring a larger separation between the classes.

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