In machine learning, what is a support vector machine (SVM)?

A support vector machine (SVM) is a supervised learning model used primarily for classification and regression tasks in machine learning. Its fundamental concept revolves around finding the optimal hyperplane that separates data points of different classes in a high-dimensional space. This optimal hyperplane is chosen based on the support vectors, which are the data points closest to the hyperplane. By maximizing the margin between these support vectors and the hyperplane, SVM effectively classifies new data points with high accuracy.

SVM can handle both linear and non-linear classification by employing various kernel functions, which allows it to create complex decision boundaries. This flexibility makes SVM highly effective in various applications, including text classification, image recognition, and bioinformatics. The distinction as a supervised method means that SVM requires labeled training data to learn from, differentiating it from unsupervised methods like clustering, error analysis, or data compression techniques, which serve different purposes in the realm of machine learning.