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    <p>It would be nice to know if you're talking about a couple of repetitions in million samples or 10 repetitions in 15 samples.</p> <p>In general I don't find what you're doing reasonable. I think your advisor has a very good point. Your evaluation needs to be as close as possible to using your classifier outside your control -- You can't just assume your going to be evaluated on a datapoint you've already seen. Even if each data point is independent, you're going to be evaluated on never-before-seen data. </p> <p>My experience is in computer vision, and it would be very highly questionable to train and test with the same picture of a one subject. In fact I wouldn't be comfortable training and testing with frames of the same video (not even the same frame). </p> <p><strong>EDIT:</strong></p> <p>There are two questions:</p> <ol> <li><p>The distribution permits that these repetitions naturally happen. I believe you, you know your experiment, you know your data, you're the expert.</p></li> <li><p>The issue that you're getting a boost by doing this and that this boost is possibly unfair. One possible way to address your advisor's concerns is to evaluate how significant a leverage you're getting from the repeated data points. Generate 20 test cases 10 in which you train with 1000 and test on 33 making sure there are not repetitions in the 33, and another 10 cases in which you train with 1000 and test on 33 with repetitions allowed as they occur naturally. Report the mean and standard deviation of both experiments.</p></li> </ol>
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    1. POShould I remove test samples that are identical to some training sample?
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    1. COI don't think I'm assuming that I'm going to be evaluated on a datapoint I've already seen. I have a set of about 1000 training samples that were taken at various dates around the world, and a set of 33 testing samples. The testing samples were collected using approximately the same process as the training samples, and essentially the same process as any sample that will presented to the classifier in the future. Lets say we have a distribution that we're sampling from to get both datasets. If it just so happens that we get the same feature vector by chance, to me that still reflects reality.
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