Kernel SVM Explorer

Supervised Learning

Kernel SVM Explorer

Nonlinear classification through similarity and margin control.

What This Shows

Adjust the kernel behavior and see how nonlinear decision regions form. The demo gives a practical view of the trade-off between flexibility and stability: highly adaptive boundaries can capture complex patterns, but can also become sensitive to noise.

SVM RBF kernel Nonlinear models Margins Classification

Technical Challenge

Capturing nonlinear structure while preserving a stable decision rule.

My Contribution

Built a visual tool for understanding how kernel parameters reshape the classifier boundary.

Industry Transfer

Image classification, anomaly detection, quality control, risk models.

Connected Work

Computer vision pipelines and automated object detection.