This is a question from a coursera course:
Suppose we have a set of examples and Brian comes in and duplicates every example, then randomly reorders the examples. We now have twice as many examples, but no more information about the problem than we had before. If we do not remove the duplicate entries, which one of the following methods will not be affected by this change, in terms of the computer time (time in seconds, for example) it takes to come close to convergence?
a) full-batch learning
b) online-learning where for every iteration we randomly pick a training case
c) mini-batch learning where for every iteration we randomly pick 100 training cases
The answer is b. But I wonder why c is wrong. Isn't online-learning a special case of mini-batch where each iteration contains only a single training case?