[e12e]

Maybe. Or maybe this is a human-centric view. Imagine that the classifier worked on sound. A low growl could be a cat, a tiger or a submarine engine. Then the probabilities might be flipped - if it's a land animal, it might be 40/60 that it's a tiger or a cat.

A visual classifier that identify "4 moving things" might indicate some kind of land animal, or slow motion video of a Dragonfly in flight.

Sample/"evidence"-based reasoning will always have these kind of odd inconsistencies - I'm not sure if mapping such output to a logic model is an improvement. It might be - to take output from a classifier like this, and plug it into an expert system like a Prolog/datalog database or something. Or it might just end up being just as limited as those systems already are.

But when one says "tiger implies terrestrial mammal (or animal)", one is really talking about ontologies -- perhaps training the classifier to come up with things like "90% sure four legs, 60% sure fur" and plugging that into a logic based system would yield good hybrid systems?

I do think one would then loose the "magic" effectiveness of these pure(ish) learning systems though? Perhaps someone more familiar with the domains might shed some light?