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From: komodo@netcom.com (Tom Johnson)
Subject: Training data
Message-ID: <komodoE3yrHH.7v5@netcom.com>
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Date: Mon, 13 Jan 1997 20:27:17 GMT
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I am working on a problem in which there are trillions of possible inputs 
and one output.

Here is my question: what are the pros and cons of inputting 100,000 
random samples and training once on each or randomizing 5000 samples and 
then cycle train then 20 times each?

Will giving it  a wider range of inputs make it more sensitive or is it 
better to get stronger correlation on the smaller sample?

I will do both experiments, but advice from people who have been there 
will be quite helpful.

Thanks in advance for any replies.

TJ
