NO.Idea

# Perceptron

Perceptron algorithm learns the weights for features. It functions if there is a hyperplane dividing dataset into two classes, which means data is linearly seperable.

Set $a=\sum_{d=1}^D w_dx_d$. $x_d$ is the $d$th feature of an input vector(sample, instance). $w_d$ is the $d$th weight corresponding to dth feature. If $a \ge 0$, it "fires".

For mathmetical convinience, threshold can be added into weight vector as $w_0$. Meanwhile $x_0 = 1$ is added into input vector.