You seem to be talking about smoothing: how many non-human four-legged animals and non-four-legged humans have you got to see before you can estimate that human

You seem to be talking about smoothing: how many non-human four-legged animals and non-four-legged humans have you got to see before you can estimate that humans are not likely to have four legs? This is reasonably well-solved; and you either use dirichlet (laplace, add-one) smoothing or something more closely corresponding to your domain, like good-turing smoothing. This reweights the probabilities in your classifier to make sure that (by analogy) if you see a talking, thinking, four-legged banker, you will probably classify him as a human (since the other features overwhelmingly point in that direction). They all assume some measure of confidence in the features, and make sure that a feature that has been observed once or twice will not move the class boundaries too much.