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*> From: "Mason Corinne" <CJM395@psy.soton.ac.uk>
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*> Date: Tue, 28 May 1996 09:41:06 GMT
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*>
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*> Exclusive Or (XOR) refers to a situation whereby a decision is based
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*> on one, and only one, of two conditions being satisfied. For
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*> instance, if I dislike crowds I may decide to go to the beach if it
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*> is sunny, or if it is a bank holiday, but not both.
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*> This is a Boolian logic function, and if each condition is assigned
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*> a value of 1 if it is met, and 0 if it is not met, then a table can
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*> be drawn up representing this, with values or 0 and 1 indicating
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*> the decision:
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*>
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*> 1 0
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*> 1 0 1
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*> 0 1 0
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*>
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*> This is a difficult concept for the human mind to grasp; we usually
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*> function using and/or conditions. For a network, it constitutes the
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*> basis of the XOR problem. How can a network be constructed so that
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*> it will arrive at the the correct output when the input data is based
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*> on XOR reasoning, and has nothing in common?
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Nothing in common except the feature "A XOR B"...

*> A network consists of a set of units that are each connected to all
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*> the units of the next layer, but can only communicate with
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*> each other by means of very simple signals.
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Not sure what that last phrase meant: compared to what?

*> The basic 2 layer perceptron is not capable of such processing, and
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*> this was Minsky's critique. What is required to accomplish XOR
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*> processing is a network such that
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*> if the input is a pair of binary digits (which can be 0 or 1),
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*> and the output is another binary pair, for the output value to be 1
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*> of one of the inputs is 1, but 0 if neither or both is 1.
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*> The answer to how this kind of decision can be made lies in the
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*> network having one or more hidden layers between the input and output
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*> layers. The units of the hidden layer are isolated from the networks
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*> environment, and the connections pass from the input layer through
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*> the hidden layer to the output layer. Each unit at a level is
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*> connected to all units of the next higher layer.
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*> The units can only transmit simple numerical values - the input
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*> receives 1 or 0 and sends an output value of 1 or 0 along each of its
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*> connections with other units. Each connection has a weight which is
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*> either positive, negative or 0, and each unit has a bias. The
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*> incoming value is multiplied by the weight on each of
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*> its connections, and the sum of the products is added to the bias
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*> that is associated with each unit. The resulting value is then
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*> assigned an activation value of 0 or 1, according to the threshold of the
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*> unit, and if the unit is thus activated it continues to propogate its
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*> value to the output layer via another weighted connection.
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*> Another advantage of this system is that changing the weights
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*> allows a network to learn from past experience,
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*> and thus improve its performance through the process of
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*> backpropogation.
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Good! To put it over the top, integrate it with the bigger issues about

nets, symbols, categorisation, reverse engineering.

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