Support Vector Machine (SVM)
A classical machine learning algorithm that finds the optimal hyperplane separating different classes, maximizing the margin between the closest data points of each class.
How It Works
SVM finds the decision boundary that maximizes the distance to the nearest training points (support vectors). The kernel trick enables SVMs to handle non-linear boundaries by mapping data to higher-dimensional spaces.
Current Relevance
SVMs have been largely superseded by deep learning for most tasks but remain useful for small datasets, high-dimensional data (genomics), and as a simple baseline. They're still taught as a foundational ML algorithm.