1. A machine learning method comprising: providing one or more training data samples having one or more known classes; training two or more learning machines to identify said one or more known classes using said one or more training data samples; selecting the trained learning machine that optimizes a performance function dependent on one or more variables between said one or more known classes selected from the group consisting of maximizing divergence between the classes of data, n-fold cross validation, number of support vectors chosen, Vapnik-Chervonenkis (VC) dimension, ratio of all support vectors to unbounded support vectors, relative magnitude of the parameters concerned, favoring a large sigma for a Gaussian kernel, and small upper bound for Lagrange multipliers of each point; and outputting the selected trained learning machine into a computer memory.