SUPPORT VECTOR MACHINE VISUALIZER

Maximum Margin Classification with Kernel Transformations

Interactive decision boundaries • Multiple kernels • Support vector highlighting • Margin optimization

SVM CONTROLS

DATA INPUT
Click canvas to add points of selected class
KERNEL CONFIGURATION
Soft margin Hard margin
ACTIONS

SVM VISUALIZATION

Add data points to begin • Click canvas to place points
Legend
Class 0 (Red)
Class 1 (Cyan)
Decision Boundary
Support Vectors
Data Points (0)
X Y Class Support Vector Action
SVM Concepts

Support Vectors: Data points that lie closest to the decision boundary and define the margin.

Margin: The distance between the decision boundary and the nearest training points.

Kernel Trick: Transform data to higher dimensions where it becomes linearly separable.

C Parameter: Controls trade-off between margin width and classification errors.