I was going for the theory and maths behind the online perceptron algorithm and it is very easy to under stand it intuitively that on a positive mistake, you just add the x value to the w and calculate new values for w and do subtraction in the case of negative mistake

So, in both cases we move closer by 1 to the value we wanted and kinda keep rotating the plane to some degree for infinite until you get a plane that separates the two classes IFF they are linearly separable.

But then I got to know about Dual Perceptron where learning rate is 1 and you add the counter every time it makes a mistake. How does this algorithm work. Just in simple case intuitively. For maths, I have the original research papers.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.