ML0044 Perceptron

The perceptron is a simple linear classifier that computes a weighted sum of input features, adds a bias, and applies a step function to produce a binary decision. The perceptron works well only for data sets that are linearly separable, where a straight line (or hyperplane in higher dimensions) can separate the classes.

The perception output can be calculated by

Where:
is the predicted output (0 or 1)
is the weight vector
is the input vector
is the bias term
is the activation function (typically a step function)

Below shows a perceptron diagram.

Limitations for using perception:
(1) Linearly Separable Data Only: Cannot solve problems like XOR, which are not linearly separable.
(2) Single-Layer Only: Cannot model complex or non-linear patterns.
(3) No Probabilistic Output: Outputs only binary values, not confidence or probabilities.