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From: kingsley@hpwrce.HP.COM (Kingsley Morse)
Newsgroups: comp.ai.neural-nets
Subject: Re: expectancy
Message-ID: <3430006@hpwrce.HP.COM>
Date: 14 Aug 90 17:56:40 GMT
References: <7035@eos.UUCP>
Organization: Ye Olde Salt Mines
Lines: 42

It all hinges on whether the input vectors specify which output nodes should
be boosted for each input vector. In other words, the training data must
contain:
1.) Many patterns
2.) Each pattern must have:
   a.) an input vector
   b.) an output vector

For example, the training data should have I inputs, O outputs, and P patterns.

    pattern         inputs           outputs
  --------------------------------------------------
      1       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O
      2       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O
      3       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O
      .       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O
      .       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O
      P       1 2 3 4 5 ....I     1 2 3 4 5 6 ....O

So the total amount of data is Px(I+O).

Given that the training data has the necessary stuff, the next step is to
decide what connections to put in the network. A typical connectivity is a
three layer net. The input layer has I "neurons". The output layer has
O "neurons". Inbetween these layers is a hidden layer, whose number of 
neurons is less well defined. If the training data has little redundancy,
the number of hidden layer "neurons" can approach the number of patterns, P.
I recommend starting with less than P hidden layers and adding more to 
improve the network's performance. The drawback is that as you add hidden
layer "neurons", the training time usually increases a lot.              

If we use H "neurons" in the hidden layer, then we can determine how many
connections the network will have; (IxH)+(OxH). Each connection has a
weight which will be adjusted during training. These are the core of the
neural network. A training algorithm such as backpropagaition will fine tune
these weights so that the input patterns will be transformed into the
correct output patterns. 

I hope this helps, if you have any more questions I'll answer them as best I 
can.

							Kingsley