In machine learning, a support vector is a point in a feature space that helps define the boundary between different classes in a classification problem. In a two-class problem, the goal is to find a hyperplane (a line or a plane in a high-dimensional space) that separates the two classes as well as possible. Support vectors are the data points that are closest to the hyperplane and are used to define it.
The idea behind support vector machines (SVMs) is to find the hyperplane that maximizes the margin, which is the distance between the hyperplane and the closest data points from each class (the support vectors). The larger the margin, the better the classifier is at generalizing to new data.
One of the advantages of SVMs is that they can handle non-linearly separable data by using a technique called the kernel trick. This technique allows SVMs to transform the data into a higher-dimensional space where it becomes linearly separable.
Another advantage of SVMs is that they are effective in high-dimensional spaces, which is common in many real-world problems. SVMs are also relatively robust to overfitting, which means they can generalize well to new data, even when the training data is limited.
SVMs can be used for classification and regression problems, and have been applied in various fields such as bioinformatics, text classification, and image recognition.
In summary, Support vector machines (SVMs) are a type of supervised learning algorithm that can be used for classification and regression problems. They aim to find a hyperplane that separates different classes in the feature space as well as possible. Support vectors are the data points that are closest to the hyperplane and help define it. The main advantage of SVMs is their ability to handle non-linearly separable data and their effectiveness in high-dimensional spaces.