Question: As in the previous problem, consider a classification problem in which each instance consists of d features x1,...,xd , each of which can take on

As in the previous problem, consider a classification problem in which each instance consists of d features x1,...,xd , each of which can take on only the values 0 or 1. A feature vector belongs to class 0 if x1 + x2 +···+ xd is even

(i.e., the number of 1’s is even) and it belongs to class 1 otherwise. Can this problem be solved by a single perceptron? A three-layered network? Why or why not?

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