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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.

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