Support Vector Machine

Fri, 08 May 2026 00:00:00 +0000

Support Vector Machine (SVM) #

Support Vector Machine (SVM) is a supervised machine learning algorithm used for:

Classification (most common)
Regression (SVR – Support Vector Regression)

It connects many earlier ideas:

classification and decision boundaries
linear classifiers
margins
optimisation
constrained optimisation
kernels for non-linear data

SVM is a discriminative classifier.

That means it does not try to model how each class is generated.

Instead, it tries to find the best separating boundary between classes.

Nonlinear SVM

Thu, 28 May 2026 00:00:00 +0000

Nonlinear SVM #

A linear SVM works well when the data can be separated by a straight line or hyperplane. When the data is not linearly separable in the original input space, nonlinear SVM maps the data to a higher-dimensional feature space where a linear separator may exist.

Key takeaway: Nonlinear SVM uses the kernel trick. Instead of explicitly mapping ( x )

to ( \phi(x) )

, we compute inner products in the feature space using a kernel:

Kernel Trick on Arshad Siddiqui

Support Vector Machine

Support Vector Machine (SVM) #

Nonlinear SVM

Nonlinear SVM #