Support Vector Machines find optimal hyperplanes to separate classes in data from "summary" of Machine Learning by Stephen Marsland
Support Vector Machines are a type of machine learning algorithm that works by finding the optimal hyperplane to separate classes in data. This hyperplane is the decision boundary that best separates the data points into different classes. The goal of Support Vector Machines is to find the hyperplane that maximizes the margin between the classes, which helps improve the generalization performance of the model.
The optimal hyperplane is found by maximizing the margin between the closest data points from each class to the hyperplane. These data points are called support vectors because they are crucial in defining the decision boundary. By maximizing the margin, Support Vector Machines are able to find a hyperplane that not only separates the classes but also generalizes well t...
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