Solved – Why perceptron is linear classifier

It is said that perceptron is linear classifier, but it has a non-linear activation function f = 1 if wx – b >= 0 and f = 0 otherwise

If i will use some non-linear function on linear combination of my data, i think i will get a non-linear classifier. Why it is false?

It is called a linear classifier because its decision boundary is given by a (linear) hyperplane. Such a hyperplane is given by the set ${ x | w^tx =b }$ which thus splits $mathbf R^n$ into two classes, ${x | w^tx leq b}$ and ${ x | w^tx > b}$. You can think of $w$ as the normal vector to this hyperplane and $b$ as an offset by which you shift the hyperplane.

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