Discussion
In Delta Rule for error minimization
Answer: B
The delta rule is a gradient descent rule for training a single layer of neurons: each weight is changed in proportion to the difference between the desired output and the actual output, multiplied by the input on that line. So the weights are adjusted with respect to the error, the difference between desired and actual output. Adjusting them with respect to the change in output alone, or the difference between input and output, ignores the target, and 'none of the above' is not right because the second option is the rule. The answer is option (B).
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