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3 Neural Network

Suppose that a neural network learner uses the network corresponding to the rule:
predicted_prop(Obj,d,V) <-
    V is f(w_0 + w_1*I_1 + w_2*I_2 + w_3*I_3).
where f is the sigmoid function.
f(x)= 1/(1+e-x)
(The only property of f you need for this exam is that f(x) > 0.5 if and only if x>0.)

Suppose that, after learning, the parameters had the following weights:

w0 -3
w1 2
w2 2
w3 4
Suppose the neural network classifies as true any example where the predicted value for d is greater than 0.5
  1. How is example e1 classified, where e1 is defined by:
  2. Give an example that is classified differently by the neural network and the decision tree
    (which is equivalent to the example decision tree given in the previous problem).
  3. Draw a decision tree that represents the same Boolean function as that represented by the neural network.
  • Solution to part (a)
  • Solution to part (b)
  • Solution to part (c)

  • Computational Intelligence online material, ©David Poole, Alan Mackworth and Randy Goebel, 1998

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