reiter@endor.harvard.edu (Ehud Reiter) (12/06/88)
Is anyone aware of any empirical comparisons of back-propogation to other algorithms for learning classifications from examples (e.g. decision trees, exemplar learning)? The only such article I've seen is Stanfill&Waltz's article in Dec 86 CACM, which claims that "memory-based reasoning" (a.k.a. exemplar learning) does better than back-prop at learning word pronunciations. I'd be very interested in finding articles which look at other learning tasks, or articles which compare back-prop to decision-tree learners. The question I'm interested in is whether there is any evidence that back-prop has better performance than other algorithms for learning classifications from examples. This is a pure engineering question - I'm interested in what works best on a computer, not in what people do. Thanks. Ehud Reiter reiter@harvard (ARPA,BITNET,UUCP) reiter@harvard.harvard.EDU (new ARPA)