Xref: utzoo comp.ai:2788 comp.ai.neural-nets:357 Path: utzoo!utgpu!watmath!clyde!att!osu-cis!tut.cis.ohio-state.edu!bloom-beacon!husc6!endor!reiter From: reiter@endor.harvard.edu (Ehud Reiter) Newsgroups: comp.ai,comp.ai.neural-nets Subject: Back-propogation question Message-ID: <766@husc6.harvard.edu> Date: 5 Dec 88 17:23:18 GMT Sender: news@husc6.harvard.edu Reply-To: reiter@harvard.harvard.edu (Ehud Reiter) Organization: Aiken Computation Lab Harvard, Cambridge, MA Lines: 19 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)