Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!clyde.concordia.ca!uunet!tut.cis.ohio-state.edu!cs.utexas.edu!wuarchive!ukma!uflorida!fish.cis.ufl.edu!fishwick From: fishwick@fish.cis.ufl.edu (Paul Fishwick) Newsgroups: comp.ai.neural-nets Subject: Re: back-prop NNs and `SAS' regression! Message-ID: <21541@uflorida.cis.ufl.EDU> Date: 19 Dec 89 18:20:27 GMT References: <220700005@uxe.cso.uiuc.edu> <1989Dec18.210859.23621@wuche2.wustl.edu> <21539@uflorida.cis.ufl.EDU> <1989Dec19.172314.16051@wuche2.wustl.edu> Sender: news@uflorida.cis.ufl.EDU Reply-To: fishwick@fish.cis.ufl.edu (Paul Fishwick) Organization: UF CIS Department Lines: 28 In article <1989Dec19.172314.16051@wuche2.wustl.edu> joshi@wuche2.UUCP (Amol Joshi) writes: > ... > BP-nn is more like a black-box and extracting >knowledge like the exponential dependencies etc is impossible from the information >about weights alone. am i missing something? >:amol > I agree with Amol that extracting information from NN models directly is more difficult. This is a general problem when analyzing any nonlinear system. However, some NN model properties may be proved directly by studying the set of equations that represent the neural network. On a slightly different note, I think that is important that we always remember that a neural network, like a signal flow graph, is just a convenient representation for a set of equations (unless one is interested in studying the neurophysiology aspect -- where structure of the network may represent biological structure). Any thoughts? -paul fishwick +------------------------------------------------------------------------+ | Prof. Paul A. Fishwick.... INTERNET: fishwick@bikini.cis.ufl.edu | | Dept. of Computer Science. UUCP: gatech!uflorida!fishwick | | Univ. of Florida.......... PHONE: (904)-335-8036 | | Bldg. CSE, Room 301....... FAX is available | | Gainesville, FL 32611..... | +------------------------------------------------------------------------+