Path: utzoo!attcan!uunet!wuarchive!zaphod.mps.ohio-state.edu!uakari.primate.wisc.edu!aplcen!jhunix!ins_atge
From: ins_atge@jhunix.HCF.JHU.EDU (Thomas G Edwards)
Newsgroups: comp.ai.neural-nets
Subject: Re: Networks for pattern recognition problems?
Message-ID: <5874@jhunix.HCF.JHU.EDU>
Date: 19 Jul 90 21:45:15 GMT
References: <23586@boulder.Colorado.EDU> <5856@jhunix.HCF.JHU.EDU> <8462@ur-cc.UUCP>
Reply-To: ins_atge@jhunix.UUCP (Thomas G Edwards)
Organization: The Johns Hopkins University - HCF
Lines: 22

In article <8462@ur-cc.UUCP> mek4_ltd@uhura.cc.rochester.edu (Mark Kern) writes:
>In article <5856@jhunix.HCF.JHU.EDU> ins_atge@jhunix.UUCP (Thomas G Edwards) writes:
>>That's a difficult statement to argue against.  I do not recall any
>>neural network techniques for pattern recognition which _perform_
>>notably better than traditional pattern recognition techniques.
>>
>
>	I hope I did not take the quote too far out of context. I'm not
>sure what the underscores around the "perform" mean.  

I was definately underspecific.  I meant performance with respect
to percentages of incorrect recognitions.
Neural nets can be much faster than "traditional" methods once
learning has been completed.  But learning can often be a very
tedious and long task.  Of course, neural networks may not need
the kind of exacting tuning and expert knowledge "traditional"
techniques do.  Some neural models, however, don't neccessarily
live up to the above statements.  Unless you are talking about
a particular connectionist system in a particular application,
generalities often are difficult to specify.

-Thomas Edwards