## Computer Science & Engineering →Pattern Recognition Lab
→List Of Experiments

# Linear Perceptron

Linear perceptron is one of the simplest learning algorithms for a two-class classifier.
Given a set of data points in d-dimensions, belonging to two classes, *C1* and *C2*,
the algorithm tries to find a linear separating hyper-plane between the samples of the
two classes. If the samples are in one, two or three dimensions, the separating hyperplane
would be a point, line or a plane respectively. The specific algorithm that we look into is a special
case of a class of algorithms that uses gradient descent on a carefully defined objective function
to arrive at a solution.