Are there perceptrons that maximize the average margin, rather than the minimum?
This is what a soft-margin SVM is already doing, except that the average has a min on the inside: $\sum_i min(h,d(x_i,c_i,w))$ where $d(x_i,c_i,w)$ is the distance of the point $x_i$ from the classification hyperplane, signed with a positive sign if it is on the correct side as determined by the target $c_i$, and $h$ is the maximum distance on the correct side you're willing to give "credit" for.  If you set $h$ to a large enough value, this is what you have above.  And, this is precisely a soft-margin linear SVM.