Support vector machines find the optimal hyperplane to separate data points from "summary" of Machine Learning by Ethem Alpaydin
Support vector machines (SVMs) are a powerful tool in machine learning for binary classification tasks. The main idea behind SVMs is to find the hyperplane that best separates the two classes of data points in the feature space. This hyperplane is called the optimal hyperplane because it maximizes the margin between the two classes. The margin is the distance between the hyperplane and the closest data points from each class, also known as support vectors. By finding the optimal hyperplane, SVMs are able to generalize well to unseen data and make accurate predictions. The optimal hyperplane not only separates the data points but also maximizes the margin,...
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