Supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.
Works by finding an optimal hyperplane that maximizes the margin/distance between different data classes, which improves prediction accuracy and generalization to new data.
More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite-dimensional space, which can be used for classification, regression, or other tasks like outlier detection.