Path: utzoo!attcan!uunet!samsung!think!yale!cmcl2!lanl!opus!ted From: ted@nmsu.edu (Ted Dunning) Newsgroups: comp.ai.neural-nets Subject: Re: What good are neural nets? Message-ID: Date: 22 Mar 90 15:54:33 GMT References: <68764@aerospace.AERO.ORG> <2355@rnd.GBA.NYU.EDU> <14746@phoenix.Princeton.EDU> Sender: news@nmsu.edu Organization: NMSU Computer Science Lines: 31 In-reply-to: kpfleger@phoenix.Princeton.EDU's message of 22 Mar 90 05:48:07 GMT In article <14746@phoenix.Princeton.EDU> kpfleger@phoenix.Princeton.EDU (Karl Robert Pfleger) writes: I think part of the point of neural nets it not that they add anything new to the solution of any _particular_ problem, but that because they _do_ solve problems with less a priori structure they are more general. this looks right at first, but in fact, there is a large amount of tweaking which makes this claim of little a priori structure much less compelling. The same neural net can learn to solve many different problems, and possibly to solve more than one problem concurrently. So, even if classical system A solves problem X better than any NN and system B solves Y better than any NN, a sufficiently large NN may be trainable to solve either X or Y, or possibly both. Part of the problem as I see it with the NNs I've heard about is that they are too small to be very general. The large the NN, the more general it will be. true. unfortunately, in most of the networks exhibited so far, the scaling of the size of the neural net or the accuracy required is prohibitive. Whereas, the NN approach generalizes automatically, without the need to create lots of different solutions. this is the claim, but where are the exemplars?