Support Vector Machines
SVMs find the hyperplane that maximizes the margin between classes. Points on the margin boundaries are support vectors — they alone determine the decision boundary.
How to Use
- Click canvas to add points (select class first)
- Drag points to move them — boundary updates live
- Choose a dataset to explore different patterns
- Press Train to fit the SVM
- Adjust C to control soft margin trade-off
- Switch kernels for non-linear boundaries
- Hover points to see details and SV status
SVM Optimization
SVM solves a constrained optimization problem to find the maximum margin hyperplane.
- Map data to feature space (via kernel)
- Find hyperplane maximizing margin
- Identify support vectors (on margin boundary)
- Classify new points by which side they fall on
Kernel Trick
Kernels compute inner products in high-dimensional space without explicit transformation.
- Linear:
K(x,y) = x·y - RBF:
K(x,y) = exp(−γ||x−y||²) - Polynomial:
K(x,y) = (x·y + 1)ᵈ
Fitted Model
Train the SVM to see model parameters.
Live Classification
Hover a point to see the classification breakdown.
10.00
| Points | 0 |
| Classes | 2 |
| Support Vectors | - |
| Margin Width | - |
| Accuracy | - |
| Kernel | Linear |
| Status | Add points to begin |