Support Vector Machine, Svm, Svm Algorithm, Svm Classifier, Machine Learning.
Euclidean distance between feature vectors. It assigns higher weights to nearby points, controlled by the scaling factor (γ). 4. Sigmoid Kernel: K(x, x’) = tanh(γx ⋅ x’ + r) Applies a sigmoid activation function to the dot product of feature vectors. It can approximate non-linear decision