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, which helps in reducing overfitting and improving the model's performance. SVMs achieve this by solving an optimization problem that involves finding the hyperplane that maximizes the margin while minimizing the classification error. In addition to finding the optimal hyperplane, SVMs can also handle non-linearly separable data by using a technique called kernel trick. The kernel trick involves transforming the feature space into a higher-dimensional space where the data points become linearly separable. By doing so, SVMs are able to find the optimal hyperplane in the higher-dimensional space, effectively separating the non-linearly separable data points.- SVMs are a versatile and powerful tool for binary classification tasks, as they are able to find the optimal hyperplane to separate data points in the feature space. By maximizing the margin between the two classes, SVMs are able to generalize well to unseen data and make accurate predictions, making them a popular choice in machine learning applications.
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