Support Vector Machines find the optimal separating hyperplane between two classes — with maximum margin to all data points. This app makes the concept of support vectors and different kernel functions tangible in real time.
The app visualises how a Support Vector Machine (SVM) draws a decision boundary between two classes of data points. The key feature: the SVM maximises the distance between the boundary and the nearest data points — the so-called support vectors. This maximisation of the margin makes SVMs robust and powerful classifiers.
A switch lets you toggle between two kernel functions: the linear kernel produces a straight decision line — suitable for linearly separable data. The RBF kernel (Radial Basis Function) enables curved, more complex boundaries and can correctly classify even non-linearly separable data.
Use the switch at the top to toggle between the Linear Kernel and the RBF Kernel. Observe how the decision boundary changes — especially when the data points are not linearly separable.
Data points can be moved, added (via the blue and red plus buttons) or removed by dragging them onto the bin icon. The decision boundary adjusts immediately to the new positions.
Interested in AI visualisations for teaching?
The VisualApps are created as a teaching and transfer project at Reutlingen University and are used in corporate training and talks.
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