SVM Dual Optimization Visualizer

SVM Dual Optimization Visualizer

SVM Dual Problem

Maximize: \( \sum_{i} \alpha_i - \frac{1}{2} \sum_{i,j} \alpha_i \alpha_j y_i y_j x_i^T x_j \)

Subject to: \( \sum_{i} \alpha_i y_i = 0 \), \( 0 \leq \alpha_i \leq C \)

Parameters

C (Regularization):

1.0

Kernel: Linear RBF (Gaussian)

Optimization Progress

Decision Boundary

Explanation

1. Dual Formulation: SVM finds α to maximize margin while minimizing error.

2. C Parameter: Controls trade-off between margin and misclassifications.

3. Kernel Trick: Maps data to higher dimensions for non-linear separation.