Question: I have a kernel perceptron with large margin ( with f ( x ) = sum ai yi k ( xi , x ) )

I have a kernel perceptron with large margin (with f(x)= sum ai yi k(xi,x)). I want to show that the updating rule ai=ai+1is equivilant to gradient descent of the hinge loss with a single parameter and example.

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