@Forget It
Every field interpret's Occam's Razor slightly differently. Edinburgh Uni's AI dept taught it thusly:
Always work on the simplest possible hypothesis that fits the observed data.
When you find a case that doesn't fit the hypothesis, then (and only then) revise (and potentially complicate) the hypothesis.
It's likely that there will be more than one "simplest" hypothesis that fits the observed data -- these form the boundary of a "search space" of possible hypotheses. Occam's Razor then "shaves" this search space down until a near-optimal hypothesis is found.
In this way, we avoid processing overly complex formulae in favour of simpler ones. This allows us to iterate through the first few generations of learning systems quite quickly.
Eventually the theory may become too complicated and you just say "close enough". I don't know whether Mr of Occam would approve of this final step.