Support Vector Machine

May 8, 2026

AI, ML

AI, ML, Support Vector Machine, SVM, Classification, Kernel Trick, Maximum Margin

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

May 28, 2026

Mathematical Foundations for Machine Learning

MFML, SVM, Nonlinear SVM, Kernel Trick, Kernel Functions, XOR

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) )