Mini toolsDefinition
A supervised learning algorithm that finds the optimal hyperplane to separate classes in high-dimensional space.
Detailed Explanation
Support Vector Machines are supervised learning models that identify the hyperplane maximizing the margin between different classes. They use kernel functions to handle nonlinear separation and can be adapted for both classification and regression tasks. SVMs maximize the margin between classes by finding support vectors (critical points near the decision boundary), making them particularly effective for high-dimensional data.
Use Cases
Image classification text categorization bioinformatics (gene classification) face recognition handwriting recognition.
