Path: utzoo!utgpu!news-server.csri.toronto.edu!clyde.concordia.ca!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 23:35:19 GMT References: <68764@aerospace.AERO.ORG> <103@cogsci.ucsd.EDU> Sender: news@nmsu.edu Organization: NMSU Computer Science Lines: 52 In-reply-to: cutrell@cogsci.ucsd.EDU's message of 22 Mar 90 20:29:58 GMT YES. now let's hear more examples, as well as examine the existing ones. but first, let's attend to cases. in particular, i refer below to the work done by doyne farmer in cnls and t-13 at los alamos and at the santa fe institute on non-linear interpolation using radial and other basis functions. the reason that this work is so pertinent here is that it performs essentially the same sort of interpolation that multi-level neural nets do, except that it requires very much less training, and when implemented efficiently, it runs orders of magnitudes more quickly than normal neural net architectures. these codes provide both a refutation to the assertion that neural nets do things better than conventional approaches, while strongly supporting the assertion that research into novel areas is important (since they were derived by examining what a neural net really does). In article <103@cogsci.ucsd.EDU> cutrell@cogsci.ucsd.EDU (Doug Cutrell) writes: The following spring to mind as immediate examples: Le Cun, Boser, Denker, Henderson, Howard, Hubbard, and Jackel of AT&T Bell Labs recently report achieving 9% rejection rate for a 1% error criterion on a U.S. Postal Service hand-written zip-code data base, with a throughput of a dozen digits per second on a 25 MFLOP DSP, including image aquistion and normalization. This data set is *EXTREMELY* noisy and consists of undoctored digitized images of zipcodes exactly as they are scribbled on real envelopes. (See Nerual Compuataion 1:4, pp. 541-551 ) this is very good. what is the performance of conventional approaches? even more to the point, what would the performance of farmer's codes? Gerald Tesauro's backgamman playing network recently defeated all other computer implementations at the recently held First Computer Olympiad in London. (See Neural Computation 1:3 pp. 321-323). even better since this is essentially a direct competition between approaches. And finally, Sejnowski's original NetTalk, while admittedly inferior to DECTalk, did not require teams of professionals in excess of one decade in order to achieve its performance! this is a _very_ poor example. other approaches have been able to learn the training set used by sejnowski in much less time and have been much more accurate on novel material.