1. A method for generating a set of X training samples from a single ideal pattern for each output class of a pattern recognition classifier, comprising: generating a system equivalent pattern for each of a plurality of classes from a corresponding ideal pattern; and applying a noise model, simulating at least one type of noise expected in a real-world classifier input pattern, to each system equivalent pattern X times to produce, for each output class, X training samples, each simulating defects expected in real-world classifier input patterns.