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From: saswss@hotellng.unx.sas.com (Warren Sarle)
Subject: Re: Normalizes the input patterns
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Date: Thu, 2 Mar 1995 19:55:57 GMT
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On Tue, 28 Feb 1995, Warren Sarle wrote:
> Another lovely example of neural net people not understanding each
> other's terminology:
> ...
> The first person is talking about normalizing variables (in statistical
> jargon), the second is talking about normalizing cases. If you folks
> can't keep straight a distinction as basic as this, I don't know if
> there is any hope for you. :-)


In article <Pine.SOL.3.91.950301072350.290C-100000@bingsun1>,
Scott Hackett <br00372@bingsuns.cc.binghamton.edu> writes:
|>
|> Mr. Sarle,
|>
|>   Thanks to you for
|>
|>      1) making me look stupid for trying to help someone
|>         with a problem

My intention was to criticize the entire neural network community, not
Scott Hackett individually. Sorry for any misunderstanding.

|>      2) not having the common sense to post an answer to the
|>         question, considering that I don't know what the hell
|>         I'm talking about.

The answer was "Yes" to Wong Tsz Cheong's question:
>   By some books, the training patterns are normalized by the following
>   equation:
>       Max = max value in the training set.
>       Min = min value in the training set.
>       range = Max - Min
>       all value of training patterns = (Old value - min) / range
>
>   It is fine for training.  However, does it mean we should remember the
>   values of Max and Min for recognition?

-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
saswss@unx.sas.com    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.
