wherepredicted_prop(Obj,d,V) <- prop(Obj,a,I_1)& prop(Obj,b,I_2)& prop(Obj,c,I_3)& V is f(w_0 + w_1*I_1 + w_2*I_2 + w_3*I_3).

(The only property off(x)= 1/(1+e^{-x})

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

Suppose the neural network classifies as true any example where the predicted value for

w_{0}-3 w_{1}2 w_{2}2 w_{3}4

- How is example
*e*classified, where_{1}*e*is defined by:_{1}prop(e_1,a,1). prop(e_1,b,1). prop(e_1,c,0).

- 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).*if(b=1,if(a=1,true,false),if(c=1,false,true)).* - Draw a decision tree that represents the same Boolean function as that represented by the neural network.

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