Exploring margin setting for good generalization in multiple class discrimination

In earlier publications, we showed that it is possible to achieve both low VC dimension and high accuracy, if we divide the given training set into a sequence of subsets each of which does admit such a solution. Here we explore in substantially more detail how the various steps in what was called “Margin Setting” impact false classification and indecision rates. A complex relationship exists between margin size, the number of steps in the process, and those two classification failures. After mapping those relationships, we offer a qualitative explanation of them.